The application of local electron correlation partitioning reveals dispersion, electrostatics and ionic substitutions all contribute significantly to the interaction energy, with a linear relationship between the ionic substitutions and the degree of charge transfer. Potential energy curves for H Intermolecular halogen bonding is a form of non-covalent interaction between a halogen atom within a molecule and a second subunit, typically one with an electron-donating group. Although an IUPAC Task Group working on the definition of a halogen bond has yet to publish its final report, the similarity to the familiar hydrogen bond produces a working definition: “The halogen bond is an attractive interaction between a halogen atom X from a molecule or fragment R[001]X in which R is a group more electronegative than X or is X itself, and an atom or a group of atoms A in the same molecule R[001]X or in a different molecule B, where there is evidence of bond formation.”The earliest known example of a halogen-bonded complex dates to 1863,3N⋅⋅⋅XY all show a Cv3 structure,A series of systematic experimental investigations into prereactive complexes of dihalogens  with Lewis bases by Legon and co-workers have produced detailed descriptions of halogen bonding in relatively small systems that are amenable to high-level ab initio theoretical studies.δi) and intramolecular (δP) electron transfer.δi is small, with a maximum of approximately 0.14 of an electronic charge transferred from PH3 to ICl. Of relevance to the present study, the fractions of electronic charge transferred in H3N⋅⋅⋅Cl2, H3N⋅⋅⋅BrCl, and H3N⋅⋅⋅ICl are roughly 0.02, 0.06 and 0.08,kσ) have also been determined for a number of H3N⋅⋅⋅XY complexes.22In addition to geometrical information, rotational spectroscopy also provides data for the magnitude of the charge transfer between the two subunits undergoing halogen bonding. By recording the changes in the halogen nuclear quadrupole coupling constants that accompany halogen-bond formation it is possible to derive an expression for both the inter-  interaction energies indicates that the MP2 method falls outside the “chemical accuracy” of 1 kcal mol−1 for most halogen bonding complexes and hence should be used only to uncover qualitative trends in halogen-bond interaction energies.Given the practical applications of halogen bonding, it is perhaps unsurprising that these non-covalent interactions have been the subject of a great number of theoretical studies  coupled cluster methods to establish theoretical best-estimates for molecular geometries and interaction energies for the halogen bonding in HmolpronZ-F12nZ-F12 herein) sets that were specifically designed for use in explicitly correlated calculations, and the aug-cc-pVnZnZ herein) sets. For the post-d elements the recently developed cc-pVnZ-F12-PP orbital basis setsnZ calculations, the aug-cc-pV(n+d)Z basis sets that are specifically designed for second row elements were used for Cl.All calculations were carried out with the F is, in this case, a previously optimised coefficient.molpro)[60]At the coupled cluster level basis set superposition error (BSSE) was compensated for by using the counterpoise (CP) method of Boys and Bernardi.Local electron correlation . In order to establish the basis set dependence of these interactions and the geometries of the resulting supermolecules, a basis set convergence study is reported in the Supporting Information. CBS limit interaction energies and high-quality geometries are presented in the next section, before the amount of charge transferred between the subunits upon halogen bond formation is investigated. Finally, analysis of the interaction energy partitioning and how it varies with intermolecular halogen bond length and angle is in the final section.nZ and VnZ-F12 families of basis sets produce almost identical results for both optimised geometries and interaction energies. VnZ-F12 is used herein due to better availability of auxiliary F12 basis sets for Br, I and At. The same study also shows that while the VDZ-F12 basis set performs well for intramolecular bond lengths and angles, the VTZ-F12 basis is required for a well-converged description of the intermolecular distance. Cv3 symmetry) geometrical parameters for the H3N⋅⋅⋅F2, H3N⋅⋅⋅Cl2 and H3N⋅⋅⋅ClF complexes. The optimised N[001]H bond lengths are not shown, but they were found to be 1.012 Å in all cases.The basis set convergence study presented in the Supporting Information, indicates that the AV3N⋅⋅⋅F2,3N⋅⋅⋅Cl2,3N⋅⋅⋅ClF)3N⋅⋅⋅F2 intermolecular distance is only slightly shorter than that in H3N⋅⋅⋅Cl2, with H3N⋅⋅⋅ClF shorter by approximately 0.36 Å.−1, VTZ-F12 by 0.36 kcal mol−1, and VQZ-F12 by 0.13 kcal mol−1. All of these basis set incompleteness errors are within chemical accuracy of the CBS estimates. The CBS data also demonstrate that while the halogen bonding in H3N⋅⋅⋅F2 is quite weak, the interaction in the H3N⋅⋅⋅AtF complex is predicted to be approximately an order of magnitude greater at −20.32 kcal mol−1. In general, the interaction energies show that as the difference in electronegativity between the halogen elements X and Y increases, the strength of the interaction also increases. This is mirrored in [31]Estimates of the CCSD(T)-F12b/CBS interaction energies were produced at the VTZ-F12 CP-optimised geometries using the CCSD(T)-F12b/VTZ-F12 and CCSD(T)-F12b/VQZ-F12 single point energies, and are presented in 3N⋅⋅⋅BrF stabilisation energy is approximately 2.3 kcal mol−1 higher than the CBS estimate  produced in the present investigation. This is entirely a basis set effect, as with a complete basis the CCSD(T) and CCSD(T)-F12b methods would produce identical results. While the interaction energies in 3N⋅⋅⋅Cl2 is roughly 2.7 times larger than that for H3N⋅⋅⋅F2, and the same is true for the calculated interaction energies. This suggests that if a H3N⋅⋅⋅AtF prereactive complex could be produced and analysed, the force constant should be around 50 N m−1.The results of Karpfen are referred to as both interaction energies and stabilisation energies in the original publication,3N⋅⋅⋅ClF where the relaxation energy is in excess of 1 kcal mol−1, and in all cases the relaxation energy has little basis set dependence. The corresponding effect of complexation on the intramolecular geometries is detailed in N-H angles in ammonia. The effect on the N[001]H bond lengths is negligible. It is noted that the experimental value of ΔR(X-Y) for H3N⋅⋅⋅Cl2 is +0.014 Å,Data in the Supporting Information show that as the halogen bond becomes stronger the relaxation energy (the difference between interaction and stabilisation energy) becomes larger. This is especially obvious for H3N⋅⋅⋅XY has been assessed via the natural bond orbital (NBO) method.The amount of intermolecular electron transfer on formation of H3N⋅⋅⋅XY, with the symbols indicating the identity of halogen X. The fractions of an electronic charge transferred are also tabulated in the Supporting Information. It can be seen that the correlation between charge transfer and interaction energy is almost linear for the lighter halogens, but this begins to tail off when X=I or At. This change in trend is further investigated in terms of correlation energy partitioning in the next section. The position of ClF also appears to be something of an outlier in the lighter halogens, with a greater amount of charge transfer than might be expected from the interaction energy. The NBO derived charge transfer can be compared to the experimental δi values, and it is observed that theoretical values are a little larger than experimental, for example, on formation of H3N⋅⋅⋅BrCl δi is roughly 0.06 of an electronic charge, with a corresponding theoretical value of 0.10. Nevertheless, there is good qualitative agreement in all cases where experimental data is available, and Mulliken outer-type complexes are indicated throughout.3N, ClF, and H3N⋅⋅⋅ClF. The negatively charged cap on isolated ammonia corresponds to the lone pair, and the area of positive charge on chlorine is the σ-hole. It can be seen that on formation of H3N⋅⋅⋅ClF an area of somewhat negative charge (yellow/orange) is located on fluorine, with the negative charge associated with the lone pair no longer visible.Further qualitative evidence of both the charge transfer and electrostatic interactions between ammonia and XY can be seen in 3 can be obtained from a partitioning of the LMP2 interaction energy into contributions from different classes of excitations. Specifically, these are intramolecular correlation (Eintra-corr) effects, dispersive coupling (Edisp), dispersion exchange (Edisp-exch), and ionic (Eionic) substitutions.ESCF) to give a total LMP2 interaction energy. 2 and AtI as in these two cases the intramolecular bond between the halogen atoms is longer than the intermolecular halogen bond .A comparison of the LMP2 interaction energies with the CBS results reported in 2, the HF interaction energy is attractive and forms a relatively large contribution to the overall interaction energy. As this ΔESCF term includes the electrostatic energy that is postulated to play a large role in the halogen bonding of such complexes,3N⋅⋅⋅F2 is weak. The correlation correction to the electrostatic energy forms part of Eintra-corr in LMP2 partitioning , which is repulsive for all of the H3N⋅⋅⋅XY systems. The Edisp-exch term is small in all cases, but the dispersive coupling contribution is attractive, and for all but the lightest dihalogens it is relatively constant at 3–4 kcal mol−1.Analysing the components of the interaction energy in Eionic is shown in Eionic is demonstrated to be almost linear, with a coefficient of determination (R2) of 0.86. If the three astatine containing dihalogens, which visually appear to be outliers, are removed from the set the correlation becomes significantly stronger with R2 = 0.95. This again suggests that the halogen bonding interaction is subtly different for these heavy halogen containing complexes.It can also be seen that the second most important term in the overall interaction energy is the ionic substitutions, implying that this term is significant in the formation of halogen bonds. This class of excitation represents the promotion of one electron from an occupied orbital on one subunit to the virtual orbital space associated with the second subunit, and is accompanied by a single excitation located solely on the second subunit. Hence, this type of double excitation sees electronic charge transferred from one subunit to the other and a plot of the NBO derived electronic charge transfer against 3N⋅⋅⋅ClF resulted in a larger amount of charge transfer than might be expected from the magnitude of the interaction energy. Further evidence for this can be seen in 3N⋅⋅⋅ClF interaction energy has a large Eionic component, representing 84% of the total interaction energy compared to somewhere between 50–70% for most of the other complexes. It could be reasoned that this is due to ClF being the heteronuclear dihalogen with the two most electronegative elements. The high electronegativity of fluorine withdraws electron density from chlorine, which in turn is also highly electronegative resulting in more charge transfer from ammonia. It is noted that the repulsive Eintra-corr term is also relatively large for this system. Eionic in Eionic contributions  along with large ΔESCF contributions, providing some insight into the subtly different interactions for these heavier dihalogens. This is also consistent with a previous investigation of formaldehyde⋅⋅⋅halomethane complexes, where it was demonstrated that the interaction in iodine containing complexes was dominated by electrostatic interactions, but the interaction in lighter halogens was principally dispersive.[11]The discussion of 3N⋅⋅⋅ClF potential energy surface that vary the intermolecular bond length and a H-N-Cl angle. The individual geometries were optimised at the CP-CCSD(T)-F12b/VTZ-F12 level by fixing either the bond length or angle and relaxing all other internal coordinates. −1, but it also predicts a minimum with a slightly shorter intermolecular halogen bond length. Despite these differences, examining the partitioning of the interaction energy will provide some qualitative insight into the nature of the halogen bonding interaction as the intermolecular bond is artificially stretched. Edisp-exch term is essentially negligible for all halogen bond lengths considered, while Edisp is attractive. As may be expected, all of the correlation energy components rapidly approach zero as the halogen bond is stretched, with the ΔESCF term  accounting for the attractive total interaction energy at long distance (beyond around 3.2 Å).In order to examine the dependence of halogen bonding on distance and angle, energy partitioning has been carried out on one-dimensional cuts of the HN-Cl angle (θ) are presented in θ. Imagining a simple Lewis-like structure, varying this angle moves the lone pair of electrons away from a linear extension of the intramolecular bond between the halogen atoms and thus acts as a probe of the directionality of the intermolecular halogen bond. As expected, for both methods the minimum in the interaction energy occurs at the tetrahedral angle, corresponding to Cv3 symmetry and the ammonia lone pair forming a linear extension to XY. LMP2 overestimates the strength of the halogen bond by 1.18 kcal mol−1, but it can be seen that the two interaction energies become much closer as the angle moves away from tetrahedral. It is perhaps surprising that the interaction energy curve has a relatively shallow gradient, for example, when θ=90° the IE is roughly −10 kcal mol−1, and at θ=65° it is approximately −5 kcal mol−1. This suggests that although the halogen bond prefers to adopt the Cv3 geometry, there is still appreciable attractive interactions occurring at relatively perturbed angles and it may be possible to exploit this reduced directionality in the practical applications of halogen bonding.The interaction energies resulting from the relaxed potential energy scan of the H-θ can be obtained from dispE contribution has no dependence on θ and the slight variations in this term may be attributed to changes in the intermolecular distance as the geometry is relaxed at a fixed angle. The ΔESCF and Eionic contributions both pass through minima at the geometry corresponding to the interaction energy minimum, with a maximum in the repulsive Eintra-corr term. As θ approaches 50° most of the contributions are close to zero, with only Edisp and Eionic accounting for most of the interaction energy. It appears that the sum of Eionic and Edisp dominates the interaction throughout, with the sum of ΔESCF and Eintra-corr relatively constant at approximately +1 kcal mol−1. Figure S2 in the Supporting Information shows that there is again a linear relationship between the NBO derived charge transfer and the magnitude of Eionic for different values of θ. A possible interpretation of the interaction energy partitioning could be that when the angle is varied the electrostatic contribution is small and the halogen bond is composed mostly of dispersion and charge transfer, which is somewhat different to the bond stretching case. It can be expected that other dihalogens will display similar trends, recalling the differences in 3N⋅⋅⋅BrCl potential energy scans and partitioning presented in Figures S4 and S5 in the Supporting Information.Some insights into the forces involved in the halogen bond at different values of Given the numerous computational studies of halogen bonding, there has been surprisingly little effort made to establish benchmark quality interaction energies on prototypical systems such as dihalogens bound to ammonia. The advent of the explicitly correlated F12 methods and associated basis sets allows for the efficient production of such reference data and CP-CCSD(T)-F12b/CBS interaction and stabilisation energies have been presented for all possible dihalogens, including hypothetical astatine permutations. Previous, lower-level, studies have demonstrated that an increase in polarisability of the dihalogen produces an increase in interaction energy. The current work supports these findings and demonstrates that this trend also applies to the astatine containing molecules. A comparison with previous, conventional coupled cluster calculations indicates that F12 methods are necessary in the production of reference-quality data as convergence of the interaction energy with respect to basis set is slow.A combination of NBO analysis and local electron correlation partitioning has provided some additional insight into the nature of the non-covalent interaction. Previous experimental and theoretical investigations had noted that the overall charge transferred from ammonia to the dihalogen upon complexation is a relatively small fraction of an electron (on the order of 0.1), but there is a correlation between the amount of charge transfer and the strength of the interaction. The current investigation adds further weight to this correlation and reveals that the ClF dihalogen appears to have a larger than expected degree of charge transfer, while the heavier halogen containing complexes tend to have higher interaction energies than one might expect from the charge transferred. A linear correlation was also found between the ionic substitution excitation contribution and the degree of charge transfer, which appears significant as this is a major contribution to the overall interaction energy. As may be expected from the σ-hole concept, the HF contribution to the interaction energy, and therefore electrostatic interactions, also provides a large contribution to the more polarisable dihalogens.3N⋅⋅⋅F2 is a weak interaction, with the present analysis revealing that this is due to a repulsive HF component and a very small amount of charge transfer. Dispersion interactions provide more than 50 % of the attractive interaction energy. It has also been demonstrated that the astatine-containing (and some iodine-containing) complexes have a subtly different halogen bonding interaction; when compared to other complexes there is a smaller degree of charge transfer/ionic substitution and a larger HF contribution. With the exception of the F2 complex, the magnitude of the dispersion contribution remains relatively constant for all systems under investigation.It is relatively well-known that HN-X, and hence the relative orientation of the ammonia lone pair and the σ-hole, acts as a probe of the directionality of the halogen bond. For the ClF dihalogen it has been shown that there is still an appreciable attractive interaction of 5 kcal mol−1 at an angle of 65°, a considerable deviation from the Cv3 minimum. Partitioning of the interaction energy reveals that dispersion forces contribute almost as much as charge transfer at large angle displacements, with little contribution from electrostatics. This is in complete contrast to partitioning of the interaction as the intermolecular halogen bond is stretched. Strong directionality is a commonly-held belief for halogen-bonding interactions, meaning that the current results of a relatively shallow gradient potential energy curve may have important implications in practical applications such as crystal engineering and rational drug design. Further investigations are currently underway to discover how these findings translate to larger halogen bonded systems.Adjusting the angle formed between H-


Mammographic microcalcifications represent one of the most reliable features of nonpalpable breast cancer yet remain largely unexplored and poorly understood.in vitro mineralisation potential of a panel of mammary cell lines. Primary mammary tumours were produced by implanting tumourigenic cells into the mammary fat pads of female BALB/c mice.We report a novel model to investigate the in vitro model. We propose a mechanism for mammary mineralisation, which suggests that the balance between enhancers and inhibitors of physiological mineralisation are disrupted. Inhibition of alkaline phosphatase and phosphate transport prevented mineralisation, demonstrating that mineralisation is an active cell-mediated process. Hydroxyapatite was found to enhance in vitro tumour cell migration, while calcium oxalate had no effect, highlighting potential consequences of calcium deposition. In addition, HA was also deposited in primary mammary tumours produced by implanting the tumourigenic cells into the mammary fat pads of female BALB/c mice.Hydroxyapatite (HA) was deposited only by the tumourigenic cell lines, indicating mineralisation potential may be associated with cell phenotype in this This work indicates that formation of mammary HA is a cell-specific regulated process, which creates an osteomimetic niche potentially enhancing breast tumour progression. Our findings point to the cells mineralisation potential and the microenvironment regulating it, as a significant feature of breast tumour development. Type II calcifications are composed of calcium phosphate, mainly hydroxyapatite (HA), which are grey-white, opaque and form ovoid or fusiform shapes with irregular surfaces . The MCF10a cells were maintained in DMEM/F-12, 5% horse serum, 0.5 μg ml–1 hydrocortisone, 100 ng ml–1 cholera toxin, 0.2 mg ml–1 epidermal growth factor, 10 μg ml–1 bovine insulin and 1% penicillin/streptomycin. The Hs578T and Hs578Ts(i)8 cells  and Biosera .The murine mammary adenocarcinoma 4T1 cell line was generously provided by Dr Fred Miller  . The mur5 cells per well and Hs578T and Hs578Ts(i)8 cells were seeded at 7.5 × 104 cells per well. The following day (day 0) the cells were treated with regular growth media or an osteogenic cocktail (OC)  ±10−7 M dexamethasone). The effects of additional exogenous factors were also investigated including inorganic phosphate (Pi) , phosphonoformic acid (PFA) , levamisole , alkaline phosphatase (ALP) , OPN  and inorganic pyrophosphate (PPi) . Cells were grown for up to 28 days in a humidified incubator at 37 °C and 5% CO2. Mineralisation was assessed using alizarin red S staining, von Kossa staining, quantitative calcium assay and Raman microspectroscopy, as described below.4T1, 4T1.2 and MCF10a cells were seeded into six-well culture plates (day −1) at 1.5 × 10ad libitum access to food and water. The animals were caged in groups of 5 or less and were acclimatised to their environment for 1 week. Cages were kept in an air-conditioned room (21–22 °C) and were on a 12-h light–dark cycle. All procedures were subjected to institutional ethics review and were carried out under the animal license guidelines of the Department of Health and Children, Ireland and in accordance with the UK Co-ordinating Committee on Cancer Research guidelines for the welfare of animals in experimental neoplasia (1998). 4T1.2 or 4T1 cells (5 × 104) were implanted into the mammary fat pad of 10- to 12-week-old female BALB/c mice. Mice were killed when tumours reached a mean tumour diameter (square root of the product of length by breadth) of 17 mm. Tumours were excised, fixed in 10% paraformaldehyde and embedded in paraffin wax. Mineralisation was assessed using alizarin red S and von Kossa staining, as described below.All mice were housed in a licensed biomedical facility  and had μm) were deparaffinised using xylene and rehydrated before staining with alizarin red S, von Kossa (using nuclear fast red counterstain) or haematoxylin and eosin (H&E). Samples were then dehydrated, incubated with xylene (30 min) and mounted with DPX.Cell monolayers were fixed with 10% formalin for 30 min and stained with alizarin red S  for 4 min. For von Kossa staining, silver nitrate (5%) was applied for 1 h under an electric lamp followed by sodium thiosulphate (5%) treatment for 2 min. For paraffin-embedded tissue, serial sections (10 M) for 1 h. The absorbance of the samples (70 μl) in combination with o-cresolphthalein  and 2-amino-2-methyl-1-propanol  was read at 572 nm . Point spectra were acquired from areas of mineral deposition initially identified visually using an optical image. Spectra were processed using Witec Project v2.0 60 software. Measurements were taken from five different points for each well, including biological repeats.Cell monolayers were fixed with 100% methanol for 30 min. Raman spectra were acquired on a Witec Alpha 300 R microscope  equipped with a 532 nm Ar-Ion laser (∼40 mW) with a spectral resolution of 3 cmRNA was extracted using trizol  and reverse transcribed using a high-capacity cDNA reverse transcription kit . Real-time PCR was carried out using the real-time PCR thermocycler  for ALP , OPN  and 18s  as an endogenous control. Samples were heated at 95 °C for 15 min, followed by a second stage composed of 15 s at 94 °C, 30 s at 55 °C and 45 s at 72 °C, which was repeated 40 times.μl) and p-nitrophenyl phosphate (100 μl) were incubated at 37 °C for 30 min and absorbance was measured at 405 nm. The results were normalised to protein content in the samples using the BCA protein assay (Novagen), which was carried out according to the manufacturer's instructions.Cells were harvested in a buffer  and kept on ice for 1 h with occasional vortexing. Samples were centrifuged at 12 000 r.p.m. for 20 min and the supernatant was used for the ALP assay. Samples (50 –1) and fast red violet LB (0.6 mg ml–1) was added to each well, which were incubated at 37 °C for 45 min. Images were recorded at × 100 magnification using the Nikon eclipse TS100 inverted light microscope .An ALP stain was also used. Cell monolayers were fixed using 100% methanol for 30 min. Tris-HCl (pH 8.5) containing naphthol AS-MX phosphate (1 mg ml5 cells per well) and the following day multiple scratch wounds were created in the monolayers. In the presence of 0.5% FBS media, the following treatments were added; 1.8–10 mM calcium, 0–10 mMβG, 0–72 μg cm–2 CO monohydrate  or 0–72 μg cm–2 HA . The HA and CO crystals were sterilised by heating at 200 °C for 90 min and sonicated briefly before their use. The cells were kept in a humidified incubator at 37 °C containing 5% CO2. Tiff images of the scratch wound assays taken at × 100 magnification at 0, 24 and 48 h were imported into Scion Image software , which is used to generate wound size measurements from still images  was detected.All statistical analysis was carried out using GraphPad Prism 5 software . A one-way analysis of variance (ANOVA) was used to analyse multiple treatment groups. A two-way ANOVA was used to analyse multiple treatments over multiple time points. in vitro model. The highly metastatic mouse mammary 4T1 cell line was grown in culture plates for up to 28 days in the presence of regular growth media (control), an OC (10 mMβG and 50 μg ml–1 ascorbic acid in regular growth media) or the OC containing 10−7 M dexamethasone (OC&dex). Cell monolayers were stained with alizarin red S and von Kossa to assess mineralisation and observed under the light microscope at × 100 magnification. Positive staining was detected in both the OC and OC&dex-treated groups using alizarin red S  and von Kossa staining , beginning on day 11 (P<0.05) and 28 (P<0.001) in the OC group compared with the control group. Although an increase in calcium was also observed in the OC&dex group over time, this was not found to be statistically significant (P>0.05). Raman microspectroscopy was used to identify the mineral species being deposited 8  compared with that found in the 4T1 (23.89±3 cm−1) and 4T1.2 (22.72±2.5 cm−1) cell lines. The data for each cell line included in The mineralisation potential of several additional murine and human mammary cell lines was also investigated. These results are summarised on al of HA . The fulPrimary mammary tumours were produced by implanting 4T1 or 4T1.2 cells into the mammary fat pads of female BALB/c mice. Serial sections of these tumours were stained with alizarin red S, von Kossa (with nuclear fast red counterstain) and H&E to assess mineralisation. One of the five 4T1 mammary tumours contained calcifications and mineralisation was detected in five out of six 4T1.2 mammary tumours. A representative mineralising 4T1.2 mammary tumour is shown in βG and Pi-induced mineralisation of 4T1 cells. 4T1 cells stained positive for calcium after treatment with either βG or Pi beginning on days 14 and 7, respectively (βG&PFA or Pi&PFA) inhibited mineralisation for up to 28 days. These results were confirmed by von Kossa staining, as shown by representative day 28 images . In contrast, there was a general trend for decreased expression of ALP mRNA in the mineralisation impaired OC&dex-treated 4T1 cells, which was statistically significant on days 7 and 14 (P<0.05). The effect of exogenous ALP was also investigated. It was found that when ALP was added to the OC, calcium levels were consistently increased compared with the OC group on days 7 (P<0.05), 14 (P<0.01), 21 (P<0.01) and 28  inhibited alizarin red S staining for up to 28 days. In contrast, levamisole had no effect on Pi-induced mineralisation, as shown by positive alizarin red S staining beginning on day 7. These findings were confirmed by von Kossa staining, as shown by representative day 28 images (βG-treated 4T1 cells over time (P<0.001 vs control and βG&lev), no increase was detected in the βG&lev group for up to 28 days (P<0.05 vs control) and similar levels were detected in the Pi&lev group (P<0.01 vs control). No statistical difference was detected between the Pi and Pi&lev groups at any time point examined for up to 28 days  and on day 11 in the mineralisation impaired OC&dex group (P<0.001). The effect of exogenous OPN was also investigated in mineralising 4T1 cells using a quantitative calcium assay (μg ml–1 OPN to the OC (OC&OPN) resulted in levels of calcium similar to the OC group by day 21 and no differences were detected between these two groups (P>0.05). Similarly the exogenous addition of PPi, a known inhibitor of physiological mineralisation, was found to have no effect on 4T1 cells (μM PPi (OC&PPi) compared with the control group by day 28 .The role of OPN was investigated using real-time RT–PCR . An incrum assay , howeverT1 cells . An incrP<0.001; . These lP<0.001). In contrast, a decrease in ALP activity was detected in the OC&dex group on days 7 (P<0.05) and 14 (P<0.001). In direct comparison, the MCF10a cell line had little or no ALP activity for any treatment group for up to 14 days.Having found that exogenous PPi and OPN do not inhibit mineralisation of 4T1 cells as expected, the endogenous levels of ALP were investigated as a potential cause. An ALP stain was used to visualise ALP activity in 4T1 cells grown for up to 14 days. A pink colour indicates positive staining for ALP and representative images are shown in βG, calcium (Ca2+), CO and HA. It was found that an increasing number of cells migrated into the scratch areas over time for all treatment groups. However, larger numbers of cells were observed in the 10 mMβG, 10 mM Ca2+ and 72 μg cm–2 HA groups compared with the control groups by 48 h (P<0.01). In contrast, no significant changes were detected for CO treatment for either 18 μg cm–2 or 72 μg cm–2 by 48 h 8 cells (15.9±1.7 cm−1) compared with all other mineralising cell lines. Narrowing of the 960 cm−1 phosphate stretching peak has been associated with lower carbonate content  generated FWHH significantly higher (21.7±1.2–23.9±3.0 cm−1) than the human Hs578Ts(i)8 cell line or previously reported data for human breast biopsies.Mineralisation of additional mammary cell lines in the presence of an OC was also confirmed, which included the metastastic mouse mammary 4T1.2 cell line and the highly invasive human mammary Hs578Ts(i)in vitro experiments because the addition of exogenous phosphate to investigate mineralisation potential is routine in mineralisation studies of numerous other pathologies, including osteoblasts and vascular smooth muscle cells in vitro , however, the detection of mineralisation in the mammary fat pads in vivo differed, with microcalcifications more common in the 4T1.2 tumours. The exact reasons for this are unclear but may be related to the cells phenotype because the 4T1.2 cell line is a variant isolated from parental 4T1 cells through single cell cloning and is known to develop overt metastases in bone, lung and lymph nodes  were investigated in order to determine the optimum conditions for mineralisation. We have shown that dexamethasone inhibits mineralisation of both the mouse 4T1 and 4T1.2 cell lines. In contrast, the presence of dexamethasone in the OC is required for the mineralisation of the human Hs578Ts(i)8 cell line. This species-specific effect has also been observed in studies of in vitro mineralisation using osteoblasts. Dexamethasone enhances mineralisation of human bone marrow cells  cotransporters (βG-induced and Pi-induced mineralisation of 4T1 cells. This indicates that, regardless of the source, internalisation of phosphate is essential for 4T1 mineralisation to occur in this model. The transport of phosphate across a membrane by a phosphate pump suggests that an active regulated process is at work and that the mineralisation observed is not simply due to precipitation within the media or the result of necrotic debris. In addition, it has been reported that the expression of the NaPi-IIb cotransporter is elevated in breast cancer tissue in comparison with normal tissue (Using the established βG-induced mineralisation, but not Pi-induced mineralisation of 4T1 cells. This suggests that ALP may have a similar function in mineralising mammary cell lines as observed for osteoblasts. Alkaline phosphatase is thought to hydrolyse organic phosphate, liberating Pi, which can be used by cells to create HA (βG-induced mineralisation takes place. This is likely due to the impaired production of Pi from βG. In contrast, impaired ALP function has no impact on Pi-induced mineralisation, as Pi is already freely available to be used by the cells for HA production. This would also explain why mineralisation is consistently detected at earlier time points for the Pi-treated cells, as hydrolysis of βG is thought to be time dependent.The role of ALP was also investigated, as this enzyme has a well-documented role in physiological HA production . An incrin vitro model of mammary cell mineralisation, as this phosphoprotein is highly expressed in bone and is known to be upregulated during bone formation (The potential role of OPN was investigated in our ormation . The endin vitro (in vitro model.The role of OPN was investigated further by examining the effect of exogenous OPN on 4T1 mineralisation, as this is known to inhibit mineralisation of osteoblasts in vitro culture conditions that resulted in mineralisation were also investigated in relation to cell migration. Using scratch wound assays it was found that 10 mMβG enhanced migration of the 4T1 cells by 40% after 48 h of treatment compared with the control group. As microcalcifications may form in response to locally high concentrations of calcium, increasing concentrations of calcium were also examined. In all, 10 mM calcium was found to increase migration of 4T1 cells by ∼30% after 48-h treatment in comparison with the control group. These data suggest that the microenvironment or niche conditions preceding, and ultimately leading to, mammary mineralisation may promote migration of 4T1 cells. Next, the functional effects of exogenous crystallised CO and HA on 4T1 cell migration were investigated. It was found that CO crystals of up to 72 μg cm–2 have no effect on cell migration for up to 48 h of treatment. In contrast, after 48 h of treatment with 72 μg cm–2 of HA crystals, 4T1 cell migration increased by 20% in comparison with the control group. As increasing concentrations of phosphate or calcium had slightly more pronounced migratory effects than the addition of exogenous crystallised HA, it is possible that the pre-mineralisation phase is of most biological significance. Mineralisation will result in sequestering of calcium and phosphate, which may be chronically released and its effects may be more pronounced in an assay carried out over a much longer time period. The increase in cell migration observed for HA treatment, but not seen with CO, lends weight to the theory that HA and the conditions, which precede its deposition may have functional biological consequences within the tumour microenvironment. These findings suggest that deposited HA may be capable of stimulating migration of the surrounding cells within the tumour microenvironment, which could contribute to breast cancer progression. We have previously reported that HA treatment of several mammary cell lines upregulates the production of MMPs (The effect of the  of MMPs . Therefoin vitro 4T1 mammary cell mineralisation (βG to glycerol and Pi. The Pi produced may then be transported into the mammary cells by the type II family of Na-Pi cotransporters. Once inside the cell, the Pi could combine with any available sources of calcium to produce HA crystals. Mammary cells have an innate ability to concentrate calcium ions, as this occurs naturally to allow lactation to take place (Based on the results discussed in this paper, we put forward a mechanism for lisation . We suggin vitro only the more tumourigenic cell lines were capable of mineralising, suggesting that our in vitro model and the proposed mechanism may be most useful for the study of calcium deposition associated with malignancy of the breast. As with all in vitro models there are limitations in the recapitulation of the complex interactions, which may take place in an in vivo scenario. Despite this our cell line model has the advantage of providing an unlimited source of homogenous material, and is free of potential contaminating stromal cells, offering the opportunity to perform reproducible mechanistic experiments in a controlled environment. Although the transposability to tumours of results obtained from cell lines universally remains a matter of debate this model represents a starting point in an area, which was previously unexplored.One of the models most notable characteristic is that the cells generate HA, a calcium species, which is found in human clinical samples. Under the conditions examined This work offers insights into the process of malignant mineralisation in breast tissue and will ultimately pave the way for a more complete understanding of the complex relationships between the mineral, the microenvironment and the differentiation state of the cells, which deposit them. As more breast cancer cases are being detected in their pre-palpable stage, largely through the presence of mammary microcalcifications, it is imperative that current research efforts reflect these developments and focus on understanding the biology underlying one of the most reliable markers of preclinical breast cancer.


Modern day information age is enriched with the advanced network communication expertise but unfortunately at the same time encounters infinite security issues when dealing with secret and/or private information. The storage and transmission of the secret information become highly essential and have led to a deluge of research in this field. In this paper, an optimistic effort has been taken to combine graceful graph along with integer wavelet transform (IWT) to implement random image steganography for secure communication. The implementation part begins with the conversion of cover image into wavelet coefficients through IWT and is followed by embedding secret image in the randomly selected coefficients through graph theory. Finally stegoimage is obtained by applying inverse IWT. This method provides a maximum of 44 dB peak signal to noise ratio (PSNR) for 266646 bits. Thus, the proposed method gives high imperceptibility through high PSNR value and high embedding capacity in the cover image due to adaptive embedding scheme and high robustness against blind attack through graph theoretic random selection of coefficients. Communication has become inevitable in everybody's routine life. Be it mails, texts, photos, or audios or videos, they get communicated in millions among billions. This in turn demands only one thing, that is, security. Information security plays pivotal role to keep the information safe. Among the prominent definitions for information security, the most vital of them is information security which is about veracity, discretion, and data availability. Though several successful methods exist, they are still in research to boost up their performance. Undoubtedly, information security is the soul of exchange of data.Steganography  and crypImage steganography can be done either in spatial domain –14 or inIn the transform domain steganography, cover image pixels are converted into coefficients by applying any one of the two-dimensional transforms. Transform coefficients work as a carrier of the secret data in the frequency domain. Three transforms, namely, discrete cosine transform (DCT) , discretThis paper is structured as follows. LL1), horizontal (LH1), vertical (HL1), and diagonal (HH1) coefficients as shown in The following Haar integer wavelet transform (HIWT) can be used to get the coefficients in integer form, and then the LSB substitution for steganography can be achieved by lossless manner. In this paper first level decomposition is adapted which results in approximation (Step  1. Process the image column-wise to get high pass filtered output (H1) and low pass filtered output (L1). The size of H1 and L1 is N × N/2Co and Ce are the odd column and even column-wise pixel values.Step  2. Take H1 and L1 for getting the approximation (LL1), horizontal (LH1), vertical (HL1), and diagonal (HH1) coefficients. Size of all four sets of coefficient is N/2 × N/2. Row-wise processing will be adapted here on H1 and L1. Separate the odd and even rows of H1 and L1 as follows: Hodd—odd row of H1, Lodd—odd row of L1, Heven—even row of H1, Leven—even row of L1. The following equations are used to get the 2D integer wavelet transform:V) can be connected by lines or arcs called edges (E). Any graph can be represented as G. In the steganographic point of view, pixels or coefficients are considered as nodes and connection between two nodes is called edge. Graceful graphs play an important role for random traversing in steganography algorithms.Graph theory is the study of points and lines. In particular, it involves the ways in which the sets of points called vertices (N), following four sequences S1, S2, S3, and S4 can be formed as follows:q” is an integer and calculated as q = N/4. N represents total number of nodes. N should be multiple of 4:qs(i) is ith element of the sequence {q, q − 1, q − 2, q − 3,…, (q − N/4) + 1}, σ(i) is ith element of the sequence (σ) obtained by random arrangement of the elements of “qs.”Since in the steganographic point of view the coefficients are considered as nodes and given the number of nodes (S3 is formed with the elements of “σ” asSequence S4 is a sequence containing “q” zerosS”:E), sequence “S”, and a sequence “A” (containing sum of elements of E and S). Graph will be generated using this table. The entire graph generation is illustrated in the example given below.N = 16.A simple example is taken for explanation; this graph is generated by considering a 4 × 4 matrix containing 16 elements. Therefore, Step 1. Generation of sequence S1: consider an integer “q”, is calculated by q = N/4. Since N is 16, q = 4. S1 is generated using (ed using :(8)S1=[Step  2. Generation of sequence S2: using S3=Step  4. Generation of the sequence S4: since q = 4,Step  5. Generation of sequence S:Step  6. Generation of a graceful graph table: Steps to generate graceful graph table are as follows.Step  6.1. Generate the sequence NS based on the number of nodes (N): NS = {1,2,…, N}.Step  6.2. Generate the edge sequence E = {1,2,…, N − 1} where the first element of sequence E will fall below second element of the sequence NS and so on.Step  6.3. Arrange the S sequence in S.Step  6.4. Compute the final sequence A by adding the elements of E and (A = E + S). Step  7. Draw a graceful graph by considering the sequences S and A. S and A are the nodes for the graph and are scanned in a zigzag manner as shown in The elements of Step  1. Consider a 512 × 512 grayscale image as the cover image (CI); theni and j are varying from 1 to 512.Step  2. Generate a secret data (SD). Here a stream of binary data is considered as secret data:Step  3. Apply histogram modification on the cover image to restrict the pixel values between 15 and 240. Now cover image is denoted as CI′:Step  4. Segment the image into 16 × 16 sized blocks.Step  5. Read all the 16 × 16 blocks one by one from top to bottom and assign numbers to all the blocks as per raster scan procedure.Step  6. Generate a random number sequence (RS) that contains unique positive integers between 1 and 1024 with the length 1024 (because the cover image is segmented into 1024 blocks of 16 × 16 size). This sequence is used to select the 16 × 16 blocks randomly. This is considered as Key1. If the first element of RS is 3 then the 3rd block will be selected first for embeddingStep  7. Apply Haar integer wavelet transform to the selected 16 × 16 block. It will result in four subbands as approximation (LL1), horizontal (LH1), vertical (HL1), and diagonal (HH1) coefficients (C) with the size of 8 × 8. In this methodology, embedding will be done in all subbands except LL1 subband to maintain good imperceptibility.Step  8. Three different Rm matrices (Key-2) will be generated with N = 64 using graceful graph corresponding to LH1, HL1, and HH1, subbands. Those matrices will be used to select the coefficients randomly in each subband.Step  9. Adaptive bit embedding procedure is adopted and the number of bits to be embedded in the selected co-efficient is given by “L” (bit length) in  in . This isStep  10. L numbers of bits are taken from SD and embedded by using LSB [C). Repeat this procedure for all the C values and the remaining subbands. sing LSB  embeddinStep  11. Repeat Steps 7 to 10 for all the 16 × 16 blocks. Step  12. Apply inverse integer wavelet transform to each 16 × 16 block and produce the stegoimage (SI), SIij ∈ {0,1, 2,3,…, 255}, where i and j are varying from 1 to 512.Exact reverse procedure should be used to extract the secret data. Step  1. Get 512 × 512 stegoimage SIij ∈ {0,1, 2,3,…, 255}.Step  2. Segment the image into 16 × 16 sized blocks.Step  3. Use key 1 to select the 16 × 16 block.Step  5. Apply integer wavelet transform to the selected 16 × 16 block. This will result in approximation (LL1), horizontal (LH1), vertical (HL1), and diagonal (HH1) coefficients. Step  6. Use key 2 for random selection of coefficients in each subband. Step  7. Use key 3 for calculating the number of bits to be extracted [xtracted  from theStep  8. Repeat the above procedure for all the C values and all 16 × 16 sized blocks.M and N are the sizes of the given image:LL1. Key 1, key 2, and key 3 provide high security against blind steganography attacks or human visual attacks. All the results are tabulated in LH1, HL1, and HH1, respectively. Embedding only in HH1 subband resulted in PSNR above 50 dB. Maximum of 144540 bits are embedded in Barbara image with embedding only in HL1. Technique of embedding data in only one subband shows high imperceptibility but less embedding capacity. However, the technique of embedding data in all the subbands except LL1 results in increased number of bits with reasonable PSNR value around 44 dB, thus reflecting the advantage of IWT domain steganography. Figures Proposed method has been evaluated with seven images with the size of 512 × 512. The performance characteristics are evaluated through MSE (mean squared error) and peak signal to noise ratio (PSNR). PSNR is calculated by using the following equation , where MSteganalysis is the blind inspection of embedded data in stegoimage. This proposed method is highly robust against the blind attacks. This method is a transform domain method, so that secret data cannot be extracted from the spatial domain. Key 1, key 2, and key 3 impart high randomness for embedding. The following numbers of iterations are required to extract the hidden information from the stegoimage generated by the proposed method:The proposed methodology is compared with the existing techniques and the TI values are tabulated in LL1 subband. This proposed method can be enhanced by using different integer transforms to attain higher PSNR and robustness can be improved further by adding multiple security methods.In the proposed work, IWT along with graceful graph offers a secure and random image steganography with high imperceptibility. Moreover, this adaptive random embedding process results in high capacity and robustness against blind attacks. Higher imperceptibility is attained when the data is embedded only in one subband, whereas higher capacity with reasonable PSNR is observed when the data is embedded in all the subbands except


Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides. Different technical solutions exist to address such large number of computations, that may be divided into three classes: a) local solutions , b) distributed solutions  and c) a combination of the previous. In any case, the most efficient technical solution to satisfy high-performance computing problems starts with an efficient local solution; such is the case of application-specific integrated circuits (ASIC). However, the number and nature of physicochemical properties may vary for different applications, making the development of ASIC too expensive. Alternatively, here we present a hardware solution using a Field Programmable Gate Array (FPGA) implementation, as a cost-effective solution for this high-performance problem. The architecture of FPGA boards can be reconfigured at the level of a hardware description language, providing further advantage in the calculation of physicochemical properties of peptides.Exhaustive prediction of physicochemical properties of peptides has different applications in biology, including mass-spectrometry data analysis Antibacterial peptides constitute one of the natural defences against infectious diseases and constitute a promising new area for the discovery of new pharmaceuticals Although different experimental approaches have been described for the identification of AP In the present work we aim to develop an FPGA solution for the exhaustive prediction of SCAPs in the virtual universe of peptide sequences. We describe an adaptation of the code to predict SCAPs, design and the implementation of the code into an FPGA card. The performance of our implementation is reported and compared with a software solution.All the implementations, but the one for the AGADIR property, were tested in a reconfigurable platform, based on a Xilinx Virtex II Pro FPGA; the target platform was the ADM-XPL, which is a commercial coprocessor board (Alphadata). The selected hardware platform have a configurable clock generator which allows to establish the work frequency from 20 MHz up to 100 MHz, using multiples of 10 MHz. Algorithms were modelled for parallelization and optimization in the Handel-C Hardware Description Language from Celoxica using the Design Kit Suite 5.0 – DK5 (Agility). Other tools used were: Visual C++ 6.0 (for interfacing the target platform with the host PC) and Xilinx ISE 9.2i (for generating the configuration file for the target FPGA).Prediction of SCAPs is accomplished by considering three features of peptide sequences: propensity to be unstructured (natively unstructured), charge and amphipathicity. These three features are predicted calculating four physicochemical properties:This is the normalized mean value of the hydrophobicity over all the amino acids in a given peptide. A peptide was considered SCAP if its MH value was within the range of 0.35 to 0.55.This is determined by Equation (1), which is based on a previous report In the present work, a peptide sequence was considered natively unfolded if the MC was above or equal to the value of C(MH):This is the pH value where a particular peptide carries no net electrical charge. A peptide was considered SCAP if it presented a pI value within the range of 10.8 to 11.8. The pI was calculated based on the following pseudo-code:first we count charged amino acids in a peptide sequence: for   {   if (protein(i) = Asp) ++AspNumber;   if (protein(i) = Glu) ++GluNumber;   if (protein(i) = Cys) ++CysNumber;   if (protein(i) = Tyr) ++TyrNumber;   if (protein(i) = His) ++HisNumber;   if (protein(i) = Lys) ++LysNumber;   if (protein(i) = Arg) ++ArgNumber;  }Then, calculate the charge contribution from each amino acid based on the corresponding ionization constant (pKa):(3.65-pH)); //C-terminal charge QN1 = −1/(1+10(3.9-pH)); //D charge QN2 = −AspNumber/(1+10(4.07-pH)); //E charge QN3 = −GluNumber/(1+10(8.18-pH)); //C charge QN4 = −CysNumber/(1+10(10.46-pH)); //Y charge QN5 = −TyrNumber/(1+10(pH-6.04)); //H charge QP1 = HisNumber/(1+10(pH-8.2)); //NH2charge QP2 = 1/(1+10(pH-10.54)); //K charge QP3 = LysNumber/(1+10(pH-12.48)); //R charge QP4 = ArgNumber/. We are doing this until NQ< = 0.i.e., amphiphilic). A peptide was considered SCAP if at least presented an μH value within the range of 0.4 to 0.6. The helical hydrophobic moment was calculated as described previously This is the sum of the hydrophobicities of the side chains of a helix of n amino acids. The length of the vector representing the hydrophobicity values is the signed numerical hydrophobicity associated with the type of side chain, and its direction is determined by the orientation of the side chain along the helix axis. A large value of μH means that the helix is perpendicular to its axis (First we set the hydrophobicity for each possible amino acid. OMH(1) = −0.40; OMH(2) = −1.12; OMH(3) = 0.17; OMH(4) = −1.31; OMH(5) = −1.22; OMH(6) = 1.92; OMH(7) = −0.67; OMH(8) = −0.64; OMH(9) = 1.25; OMH(10) = −0.67; OMH(11) = 1.22; OMH(12) = 1.02; OMH(13) = −0.92; OMH(14) = −0.49; OMH(15) = −0.91; OMH(16) = −0.59; OMH(17) = −0.55; OMH(18) = −0.28; OMH(19) = 0.91; OMH(20) = 0.50; OMH(21) =  = 0.00; OMH(22) = 1.67; OMH(23) = −1.07; OMH(24) = 0.00;Calculate the necessary Angle per residue increments, . Angle = StartAngle NumAngle = 0 for ((Angle< = StopAngle) and (NumAngle< = MAXINC)) {  NumAngle = NumAngle+1;  RadAngle(NumAngle) = Angle/360*2*3.14159265;  Angle = Angle+IncAngle;  SinSum(NumAngle) = 0;  CosSum(NumAngle) = 0;  CorSSum(NumAngle) = 0;  CorCSum(NumAngle) = 0; }Calculate the hydrophobic moment and save the maximum value and corresponding angle.Calculate the first window. Hydsum = 0; for  {HPhob = OMH (protein(Pos) );} HydSum = HydSum+HPhob; for  {  Angle = RadAngle(AngPos) * (Pos);  SinSum(AngPos) = SinSum(AngPos)+Sin(Angle) * HPhob;  CosSum(AngPos) = CosSum(AngPos)+Cos(Angle) * HPhob;  CorSSum(AngPos) = CorSSum(AngPos)+Sin(Angle);  CorCSum(AngPos) = CorCSum(AngPos)+Cos(Angle); } }Calculate the moment on each window by adding the new position to the sine and cosine sums, and subtracting the old. for  {  MaxValue(Pos−Window+1) = 0;  MaxAngle(Pos−Window+1) = 0;  HPhob = OMH( protein(Pos) );  HydSum = HydSum+HPhob;  HAve = HydSum/Window;Add the new value at the right end of the window. for  {  Angle = RadAngle(AngPos) * (Pos);  SinSum(AngPos) = SinSum(AngPos)+Sin(Angle) * HPhob;  CosSum(AngPos) = CosSum(AngPos)+Cos(Angle) * HPhob;  CorSSum(AngPos) = CorSSum(AngPos)+Sin(Angle);  CorCSum(AngPos) = CorCSum(AngPos)+Cos(Angle);Calculate the moment, and save the max.2+ Moment = Sqrt( (SinSum(AngPos)−CorSSum(AngPos)*HAve)2 );  (CosSum(AngPos)−CorCSum(AngPos)*HAve) Moment = Moment/Window; If ) {  MaxValue(Pos−Window+1) = Moment;  MaxAngle(Pos−Window+1) = StartAngle+(AngPos−1)*IncAngle; }}Substract the oldest value from the left end of the window. HPhob = OMH(protein(Pos−Window+1) ); HydSum = HydSum−Hphob; for  {  Angle = RadAngle(AngPos) * (Pos−Window+1);  SinSum(AngPos) = SinSum(AngPos)−Sin(Angle) * HPhob;  CosSum(AngPos) = CosSum(AngPos)−Cos(Angle) * HPhob;  CorSSum(AngPos) = CorSSum(AngPos)−Sin(Angle);  CorCSum(AngPos) = CorCSum(AngPos)−Cos(Angle); }}Thus, the propensity of a peptide to be unstructured is predicted by MH and MC, the charge is accounted by the IP and the amphipathicity by the μH.The proposed algorithms were first analyzed from its original implementation in FORTRAN77 and tested on a cluster of four computers with each a Pentium IV at 2.4 GHz running on Linux.5 peptide sequences (204) of 9 amino acids in length were evaluated corresponding to sequences derived from the following pattern: RAAAYXXXX, where letter X stands for any of the 20 amino acid, Y, R and A stand for Tyrosine, Arginine and Alanine, respectively. The same sequences were used to evaluate the time efficiencies obtained in our FPGA board.To evaluate the number of unstructured peptides predicted by the AGADIR score and the charge and mean hydrophobicity criteria (C(MH), see equation 2), 1.6×10The following steps were performed for the physical implementation of the Handel-C code into the FPGA:The Handel-C code is translated to VDHL code using the DK5 Handel-C Compiler.The generated VHDL code is embedded as a component into a VHDL project, which contains an I/O interface.Then, the Synthesis, Map and Place and Route processes from the Xilinx Tools (Xilinx ISE 9.2i) are executed. Finally, the configuration file for the target FPGA device is generated and downloaded to the FPGA board for testing.5 peptide sequences analyzed had features of known SCAPs, and consequently not every algorithm computed every peptide sequence. Thus, four parallel counters were implemented to account for the execution time of each hardware module. The values stored in these counters represent the total number of cycles required for each hardware process. To obtain the total execution time in hardware of each process, the value of each counter is multiplied by the reciprocal of the clock frequency, in this case 50 MHz because this frequency is lower than the maximum clock frequency possible for the design.To determine the execution time per peptide sequence, we recorded the total time reported in the hardware and software implementations and divided by the number of peptide sequences analyzed; this is reported as the average time for each implementation. Not every sequence in the 1.6×105 sequences of 9 amino acids in length generated with an infinite period random algorithm built on a unit square and evaluated with AGADIR, only 620 of these sequences (0.0388%) did not have an AGADIR score within the range of known SCAPs (AGADIR<10).In this work, we predict SCAPs by computing the charge , amphipathicity  and the propensity of a peptide sequence to be natively unfolded by combining two physicochemical properties. This last property was originally estimated using the AGADIR score Thus, here we report the use of a simpler algorithm that has been previously described to predict natively unfolded proteins We divided the prediction of each peptide in 4 basic blocks, where each block performs each one of the 4 physicochemical properties calculations (see Design and Implementation section). A functional architecture of this design is described in The hardware architecture modules include:Hydrophobicity Hardware Module (HHM), implements the MH algorithm in four storage modules (Amino acids Memory (AM), hydrophobicity Memory (HM), Peptide Charge Register (PCR) and Hydrophobicity Register (HR)) and one Length Adjusting Module (LAM).Mean Net Charge Hardware Module (MNCHM), implements the MC algorithm using a set of registers required to accelerate this calculation ; the final values were registered in the FNCR memory of this module.Isoelectric Point Charge Hardware Module (IPCHM), implements the pI algorithm; for this, several multilevel memory access in parallel are used to accelerate the computation  that is stored ultimately at the Isoelectric Point Charge Register (IPCR).Helical Hydrophobic Moment Hardware Module (HHMHM) implements the μH algorithm. This module actually computes the MC and MH values that are used by the MNCHM; it stores also the Sine (SM) and Cosine (CM) values that accelerate the μH calculation; however, this acceleration is counterbalanced by the large number of cycles required to execute this module. The values in the HM, SM and CM are stored in three temporal registers . The final result is stored in the Mean Value Register (MVR).Global Control Unit (GCU) defines the execution order of each of the hardware modules; this is currently achieved by activating one module at a time changing the control signal of the Multiplexer1 (MUX1) in each hardware module.5 peptide sequences analyzed, the same 4,984 peptide sequences were predicted as SCAPs by our FPGA board and the software version.Every peptide sequence was represented by a fixed-point number and generated by the FPGA board, thus eliminating the overhead due to the CPU-FPGA data communication. We validated our peptide representation in the FPGA comparing the results obtained using the FPGA with the software version (see Design and Implementation section) and observed 100% match in all the tested sequences: from the 1.6×10In total, our FPGA implementation uses up to 99% of logic and RAM memory and 5% of input/output resources of the FPGA itself simulated at a maximum frequency of 55.16 MHz/cycles see . Since ie.g., the decimal number 19 is represented by 5 bits in a binary code, 10011. Thus, for the peptides of 9 amino acids in length used in this study, we used up to 45-bits for each peptide. To generate all the peptide sequences tested in the FPGA card, we used a simple counter.All the physicochemical values use floating-point operations, but for our FPGA implementation these were estimated as fixed-point integer numbers. The floating-point arithmetic circuits require more space than integer representation in the FPGA. Hence, integer representation is preferred to maximize the area used in the FPGA device and allow for parallelism. In this way, each one of the 20 amino acids was represented by an integer from 0 to 19; each integer is coded in 5 bits using a binary representation: Each module was optimized in order to return one result per clock cycle. With a 50 MHz clock frequency, the execution times achieved are reported in There are different software solutions to compute physicochemical properties of peptides. Despite the high-performance computing nature of these calculations, there have been no developments to solve these in an efficient way. The FPGA's features are adequate to address the computation of physicochemical properties of peptides, because these allow testing diverse embedded codes at low cost and relatively short developing times. Additionally, having a custom FPGA implementation of these algorithms is important based on:Our interest to exhaustively explore the sequence space of peptides to identify potential SCAPs,While FPGA performance may compete with clusters of computers, the cost of running and maintaining FPGA platforms is importantly lower than those of computer clusters ande.g., predictions of proteins natively unfolded) that could benefit from the description of this work.There are many physicochemical properties used in the prediction of antibacterial peptides e.g., Mentor e.g., the pI algorithm, see Here we report an optimization and implementation into an FPGA device of four instantiations of algorithms useful to compute physicochemical properties of peptides. FPGA boards are a convenient platform to build custom computing processors and achieve high performance at a fraction of the cost of other high-performance computing solutions. FPGAs are attracting the attention of scientists in the bioinformatics area, and different approaches to design and program them are available  out of 4 physicochemical properties computed are insensitive to the amino acid order in the peptide sequence, but the helical hydrophobic moment is not. If all the properties would have this feature, only a fraction of all possible peptide sequences would be analyzed and there would be no need for an FPGA implementation. However, as noted in In our study, we show that our code has the same precision than the software version of it. Yet, the size of the four codes used up to 99% of the logic and RAM memory, preventing further replication in the tested FPGA card. However, the reported number of gates per algorithm in Besides the overall acceleration of our FPGA implementation, individually each of the 4 physicochemical properties calculations were accelerated from 17 to 195 times see  being thAnother important aspect in the development of FPGA codes is the time involved in coding low-level routines in FPGA boards. To accelerate this, we used the Handel-C language, which is based on the ANSI-C standard but with explicit parallelization constructs. Since coding in Handel-C has to be done keeping in mind the physical restrictions of the FPGA device, the designer has to consider the number of bits of every variable, types of arithmetic representation and operations. During the compilation and hardware synthesis, the code in Handel-C is transformed into a VHDL language representation. Thus, a second inspection of the code is always needed according to the FPGA platform model used.e.g., as the hardware blocks described here) for different simulation purposes. For instance, FPGA boards capable to compute physicochemical properties may be used to analyze natively unfolded peptide sequences and SCAPs in an exhaustive fashion, among others. Our results may pave the way towards that goal.Finally, it is important to note that there are many other physicochemical properties that may be relevant to embed on dedicated hardware. As more of these routines became available, these may be treated as modules on FPGA boards  APD: the Antimicrobial Peptide Database. Nucleic Acids Research 32, D590–D592); NS indicated that the peptide sequence is Not Specified in the APD and were obtained elsewhere ; Sequence reports the corresponding peptide sequence using the single-letter amino acid code. The antibacterial peptides reported to be non-structured in water solution are in indicated with a gray background and the structured ones are in white cells in the table.(DOC)Click here for additional data file.


High [mu]M concentrations of Zn2+ or selenite were strongly cytotoxic, killing the AS-30D cells by both apoptotic and necrotic ways. Both Zn2+ and selenite produced strong changes in intracellular generation of reactive oxygen species (ROS) and the mitochondrial dysfunction via the mitochondrial electron transport chain (mtETC) disturbance, the membrane potential dissipation, and the mitochondrial permeability transition pore opening. The significant distinctions in toxic action of Zn2+ and selenite on AS-30D cells were found. Selenite induced a much higher intracellular ROS level (the early event) compared to Zn2+ but a lower membrane potential loss and a lower decrease of the uncoupled respiration rate of the cells, whereas the mtETC disturbance was the early and critical event in the mechanism of Zn2+ cytotoxicity. Sequences of events manifested in the mitochondrial dysfunction produced by the metal/metalloid under test are compared with those obtained earlier for Cd2+, Hg2+, and Cu2+ on the same model system.Mitochondria of AS-30D rat ascites hepatoma cells are found to be the main target for Zn Mitocho2+ or selenite for 24 h, stained with propidium iodide and analyzed by flow cytometry as the sub-G1 fraction  and [Ca2+]. For composition of media and experimental details, see figure legends.Measurements of oxygen consumption by isolated mitochondria were conducted polarographically using a Clark-type electrode as before . The ΔΨmt-test with P < 0.05 assumed as the significance threshold.The results shown are representative or average from a minimum of three independent experiments. Data are expressed as mean values ±SE; statistical significance was analyzed, using Student's 2+ and selenite action on AS-30D cell viability. We found that high concentrations of selenite (50 μM) and Zn2+ (250 and 500 μM) killed AS-30D cells time-dependently by both necrotic and apoptotic ways  pore  agaie or Zn2+. μM concentrations of Cd2+, Hg2+, and Cu2+  on rat ascites hepatoma AS-30D cells and found that the toxicity of these three metal ions decreased from Hg2+ (most toxic) to Cu2+ (least toxic) [2+ and Cd2+ produced a high percentage of cell death by both necrosis and apoptosis, whereas Cu2+ at concentrations up to 500 μM was weakly effective. All the metals produced significant changes in the mitochondrial function and in intracellular ROS generation. Moreover, our data showed that increased ROS level alone was not sufficient to induce apoptotic and/or necrotic decay of AS-30D cells. In the case of Cd2+ and Hg2+, additional factor(s) must have been present that was/were responsible for their cytotoxic action; most likely, it was the blockage of the mitochondrial respiratory chain [2+-produced cytotoxicity was accompanied by increased ROS formation at the mitochondrial respiratory chain complex III level and the MPT pore induction [It seems important to say that previously we compared toxic action of nduction .2+ and selenite, we studied the influence of this metal/metalloid on ΔΨmito. As seen from μM of selenite, the significant decline in ΔΨmito (approximately on 30% compared to control) was observed. During the same time, 500 μM of Zn2+ produced a more than twofold decrease in ΔΨmito compared to the control. After 24 h incubation, both high Zn2+ and selenite caused the severe ΔΨmito loss of AS-30D cells (2+ (50 μM) and selenite (1 μM) did not produce any significant changes in ΔΨmito of AS-30D cells even after 24 h incubation with the cells  already after 3 h exposure with the cells. During that short incubation time, Cd2+ exerted a dose-dependent effect that became statistically significant only at concentrations of 100 μM and 500 μM. Cu2+ had a smaller inhibitory effect on ΔΨmito after 3 h, which was still statistically insignificant for 100 μM Cu2+ after 24–48 h, while 500 μM Cu2+ strongly depressed ΔΨmito after 48 h. As to Cdand Cu2+ , when me2+ and selenite, we examined their action on the cell respiration. As shown in μM of selenite, and after 48 h its magnitude was 20% of the control. In the case of high Zn2+, the uncoupled respiration rate of the cells decreased more than twofold already after 3 h, and was completely inhibited after 48 h incubation with 500 μM of Zn2+  and selenite (up to 5 μM) did not affect the uncoupled respiration rate of AS-30D cells at all incubation times under study, while 10 μM of selenite after 48 h exposure with the cells decreased significantly the uncoupled respiration rate which was 80% of the control (data not shown). It is important to say also that 50 μM of selenite evoked a significant (30%) stimulation of the resting respiration rate  significantly inhibited the cell respiration in the resting state, starting already from 3 h treatment. All other concentrations of Zn2+ and selenite under test did not produce significant effects on the resting state respiration rate of AS-30D cells. It should be noted that in the case of high Zn2+ only the inhibitory action on the resting respiration of AS-30D cells was obtained together with the strong inhibitory effect on the uncoupled respiration rate, pointing to a blockage of the mitochondrial respiratory chain.To next clarify the mechanism(s) of mitochondrial dysfunction involved in the harmful effects of Zn of Zn2+ . At the μM Hg2+ the resting state respiration was slightly but significantly increased, whereas the uncoupled respiration remained unaffected, pointing to a weak uncoupling effect of that low concentration of the metal. In contrast, at 50 μM Hg2+, all three values  were strongly depressed, indicating a potent inhibitory effect on the respiratory chain. With Cd2+, a decrease by about 30% of the uncoupled respiration was observed at 100 μM concentration and some inhibitory effect could be seen already at 50 μM concentration. Further increasing Cd2+ concentration to 500 μM increased the inhibitory effect. In contrast, Cu2+ had no inhibitory effect even at 500 μM concentration after 3 h, but exerted a weak uncoupling effect, as manifested by an increase of both steady-state and resting respiration rates. After 24 h incubation of the cells with the corresponding heavy metal, 50 μM Hg2+ and 50 μM Cd2+ produced a practically complete depression of cellular respiration, while 50 μM Cu2+ exerted a pronounced uncoupling effect; moreover, even after 48 h 50–100 μM Cu2+ did not inhibit the cell respiration, whereas 500 μM Cu2+ decreased it to 70% or below.In turn, before , we founμM of selenite enhanced in three times the intracellular ROS generation already after short incubation times (50 min and 3 h), while after 24–48 h they evoked the sharp reduction of the ROS production compared to control. In contrast, high concentrations of Zn2+ under test, namely, 250 and 500 μM, did not produce any significant changes in the ROS generation after 50 min of incubation with the AS-30D cells. Nevertheless, high Zn2+ increased moderately the intracellular ROS formation after 3 h and decreased their production in a half (compared to control) after 24–48 h of incubation with the cells (2+ (50 μM) and selenite (up to 5 μM) did not change the ROS generation of AS-30D cells after all durations of incubation used. We found also that the intracellular ROS generation changes, observed after incubation of AS-30D cells with high Zn2+ or selenite, were attenuated by CsA  of mitochondrial dysfunction produced by the metal/metalloid under test, we compared the action of Zn Figures  and 6 + was replaced by Na+ in order to monitor K+ fluxes in and out of mitochondria , which again were only partially sensitive to CsA [2+ induced a sharp oxidation of pyridine nucleotides (PN) that was inhibited by CsA, the protective action of which, however, was overridden by increase of Zn2+ load. In addition, the mitochondrial swelling produced by Zn2+ in the KCl and sucrose media was retarded by CsA and other MPT inhibitors [We found that in NaCl respiratory assay medium where KMPT pore . Howeveraddition , trace 2 of Zn2+ , trace 4e to CsA . Before hibitors .2+ on the mitochondrial function resembles very much the Cd2+ effects studied by us before  in the presence of selenite was observed in the KCl assay medium as well; in addition, we found before that in this medium selenite decreased both the respiration in St 3 and the DNP-stimulated respiration [plus Mal was only partially susceptible to CsA in opposite to the Ca2+-induced one which was completely depressed by CsA. It is worthy to note that on this type of respiratory substrates, Ca2+ produced only a transient stimulation of the resting respiration followed by the strong inhibition of the mitochondrial respiration both in this medium  as it was observed after addition of the mentioned above metal pulses (5 μM—for Zn2+or Cd2+ and 50 μM—for Ca2+) but there was the moderate and continuous PN oxidation which was only slow down by CsA under used experimental conditions. In the case of selenite, we found that not only K release  but alsothe NaCl  and in tsipation . Howeverreatment , trace 4piration . As we hexample,  and refeexample, , the inf2+ or Cu2+ on isolated rat liver mitochondrial function, we found in our previous works [mito decrease produced by these heavy metals were insensitive or weakly sensitive to CsA. Besides, CsA and other MPT pore effectors, including several mtETC inhibitors affected differently the mitochondrial swelling induced by the metals/metalloid under test [As to about action of Hgus works , 28 thatder test .2+, Hg2+, and Cu2+ as we obtained before [2+ and sodium selenite. High μM concentrations of Zn2+ or selenite were strongly cytotoxic, killing the AS-30D cells by both apoptotic and necrotic ways  compared to Zn2+ but a lower membrane potential loss and a lower decrease of the uncoupled respiration rate of the cells, whereas the mtETC disturbance was the early and critical event in the mechanism of Zn2+ cytotoxicity. In the present study, we have found that mitochondria of AS-30D rat ascites hepatoma cells are the main target not only for Cdd before , 10 but neration  and the neration , the memneration , and theneration . The sigmito found already after 3 h incubation of the cells with high selenite that indicates the uncoupling effect of the metalloid at early times of incubation. Besides, the rapid and potent burst of the intracellular ROS formation found in the presence of high selenite after 3 h incubation with AS-30D cells was partially depressed by CsA  underlying Zn2+- and selenite-induced mitochondrial dysfunction and cytotoxicity, in support to a recent series of publications on the issue [It should be mentioned that up to date the role of Zn and Se compounds in prevention and possible treatment of cancer still remains obscure due to their complex interactions with cells and tissues. Nevertheless, a lot of evidence indicates direct toxicity and proapoptotic activity towards malignant cells of externally supplemented Zn2+ , 29–31 o2+ –36. The d before –45; howeselenite , 46, 47.ensitive –23) in mensitive , 47–50 ative [2+ , 51–56 wtive [2+ . In partnsitive, , 60) in nsitive, . Despitehe issue –68. It s


Isotopic measurements of ammonium (NH+4), nitrite (NO−2), nitrate (NO−3), and N2O can now be used to track the cycling of these compounds and to infer their sources and sinks, which has lead to new and exciting discoveries. For example, dual isotope measurements of NO−3 and NO−2 have shown that there is NO−3 regeneration in the ocean's euphotic zone, as well as in and around oxygen deficient zones (ODZs), indicating that nitrification may play more roles in the ocean's N cycle than generally thought. Likewise, the inverse isotope effect associated with NO−2 oxidation yields unique information about the role of this process in NO−2 cycling in the primary and secondary NO−2 maxima. Finally, isotopic measurements of N2O in the ocean are indicative of an important role for nitrification in its production. These interpretations rely on knowledge of the isotope effects for the underlying microbial processes, in particular ammonia oxidation and nitrite oxidation. Here we review the isotope effects involved with the nitrification process and the insights provided by this information, then provide a prospectus for future work in this area.The microbial nitrogen (N) cycle involves a variety of redox processes that control the availability and speciation of N in the environment and that are involved with the production of nitrous oxide (N Although sunlight appears to partly inhibit nitrification  sinks out of the euphotic zone, it is gradually broken down into its component parts and remineralized into its inorganic forms: CO2, NH+4, and PO3−4. In oxic water columns, the NH+4 released from organic matter remineralization below the euphotic zone is rapidly oxidized to NO−3. The distribution of nitrification rates in the ocean is therefore expected to follow the distribution of NH+4 supply from organic matter remineralization, which decreases exponentially with depth  cycle converting the most reduced form of N  and ammonia-oxidizing archaea (AOA). These organisms use NH3 as their source of reducing power for CO2 fixation and energy production. Nitrite oxidation is also a chemoautotrophic process and is carried out by nitrite-oxidizing bacteria (NOB). These bacteria use nitrite (NO−2) as their source of reducing power for CO2 fixation and energy production  can also have implications for the production of nitrous oxide (N2O), a climatically important greenhouse gas. It is therefore important to know how the processes contributing to the production and maintenance of the PNM vary in space and time.Although NO−2 also accumulates in oxygen deficient regions of the water column in a feature termed the secondary nitrite maximum , anaerobic ammonia oxidation (reduction of NO−2 to N2 and oxidation to NO−3), and nitrite oxidation (oxidation of NO−2 to NO−3). Recent studies using natural abundance isotopes , (3) the role of nitrification in near-surface N2O production  the role of euphotic zone nitrification in supplying NO18O value of NO−2 produced during ammonia oxidation  × 1000) is dependent on the δ18O values of the oxygen atom sources (O2 and H2O), isotopic fractionation during their incorporation , as well as any exchange of oxygen atoms between nitrite and water (xAO) and the corresponding equilibrium isotope effect (18εeq)  × 1000.The δ2 to H2O in a 1:1 ratio during ammonia oxidation  of 11–14‰  = δ15Nα − δ15Nβ) of the N2O produced through these pathways may provide insight into the mechanisms of N2O production under different growth conditions , including both incorporation of O2 into NH2OH and production of N2O from NH2OH, was 2.9 ± 0.8‰ indicating that N2O produced from this pathway had a lower 18O:16O than the ambient O2  was −8.4 ± 1.4‰  is determined mainly by the enzymatic mechanism, rather than the substrate δ15N value  by AOA involves hydroxylamine, isotopic evidence to date shows that the N2O produced aerobically by AOA does not have a SP consistent with denitrification or nitrifier-denitrification. δ18O data also show that the N2O produced by AOA incorporates O primarily from O2, rather than from H2O, which supports production by decomposition of an intermediate, rather than from NO−2 under the conditions tested  and the heavy isotope can be preferentially shunted in one direction vs. the other. This is analogous to the branch point that has been described during the oxidation or assimilation of ammonium  and the produced NO−3. The expected δ18O3NO value produced in oxygenated seawater with little exchange is −1‰ to +1‰ (similar to δ18O2OH), resulting from a complex series of fractionation factors rather than the unfractionated incorporation of and exchange with H2O , which is dependent on temperature . This provides a unique approach to determining rates of biological processes based on static isotope measurements, without bottle incubation and associated perturbations of the system. Applications such as this move us from laboratory studies of isotope effects to a deeper understanding of the cycling of N in the environment. There are many additional examples of how knowledge of the isotope effects for nitrification has enabled advances in our understanding of the marine N cycle, and we highlight a few below.The tendency of NO2 gas from dissolved inorganic nitrogen (DIN) compounds thereby removing them from the nutrient inventory. The magnitudes of these fluxes have been estimated in many different ways: through isotope tracer experiments  observed within ODZs  with a kinetic isotope effect of −15‰, then Δδ15N at steady state should approach 40‰. If all NO−2 consumption occurs through nitrite reduction (FNXR/FNAR = 0) with a kinetic isotope effect of +15‰, then Δδ15N would be expected to approach 10‰ at steady state. The δ15N difference between NO−3 and NO−2 may therefore be diagnostic of NO−2 sinks in ODZs , Fe(III), and Mn(IV) would be thermodynamically feasible. Moreover, as mentioned above, there is independent evidence based on 15N incubations for nitrite oxidation occurring within the ODZs in the ETSP  and were used by Sigman et al.  = 0‰  would be proportional to the N fixation flux  in euphotic zone and oxygen deficient zones.A relative enrichment in 15N3NO and δ18O3NO in a hypothetical ODZ  and reduction  from NO−3 and NO−2 isotopic data. The oxidative flux is assumed to have the N and O isotopic systematics of bacterial nitrite oxidation  or NH+4 [via denitrification to ammonium (DNRA)]. Unfortunately, very little information is currently available on the N isotope effects for nitrite reduction by these processes  between 0 and 3 (FNXR representing 0–75% of NO−2 consumption) and the rate constant for exchange (kEXCH) between 0 and 1 day−1 to evaluate the effects of changes in these parameters on simulated δ15N3NO and δ18O3NO  are given in units of day3 Figure . Maximum Figures –D, and m15N3NO and δ18O3NO patterns. When FNXR/FNIR = 0, the δ15N3NO and δ18O3NO data fall along the 1:1 line prescribed by the isotope effects for nitrate reduction  values are produced. The strength of this effect is also dependent on the rate of abiotic NO−2/H2O exchange, with higher exchange rates partly diluting this effect and actually leading to positive Δ values at high extents of NO−3 consumption  anomalies at a given δ15N3NO value. Furthermore, because excess N2 does accumulate in ODZs, we know that some NO−2 is ultimately reduced to N2. Indeed, we could potentially use the stoichiometry of N2 production in ODZs to interrogate the importance of nitrite oxidation. If nitrite oxidation is not important, the standard stoichiometry  are each more effective at different points in NO−3 isotope space  signals at δ15N3NO and δ18O3NO values less than 10‰, near the base of the euphotic zone. In contrast, nitrite reoxidation is most effective at generating negative Δ signals at intermediate δ15N3NO and δ18O3NO values and extents of NO−3 consumption by denitrification, where N2 fixation has relatively little effect on the Δ. Therefore, we may be able to distinguish between the processes responsible for Δ generation by where the anomaly lies in δ15N3NO vs. δ18O3NO space, as well as from other water column indicators. For example, using a steady state model, Casciotti and McIlvin  signals at these δ15N3NO and δ18O3NO values through either N2 fixation or nitrite reoxidation alone  values  and N budget processes (N2 fixation and denitrification) and the ratio of low latitude productivity, where nutrient consumption goes to completion, to high latitude productivity, where nutrient uptake is incomplete. By comparing model results to δ15N3NO and δ18O3NO data from a variety of oceanographic profiles representing the major ocean basins, the impacts of partial NO−3 assimilation in polar regions on the N and O isotopes of NO−3 in the ocean interior, and of low latitude productivity on the 18O enrichment in preformed NO−3 was diagnosed. N budget processes (N2 fixation and denitrification) led to variations in subsurface δ15N3NO and δ18O3NO, but in their absence, the large scale steady state δ18O value of subsurface NO−3 was set by nitrate assimilation in polar regions. Nitrate uptake in the southern ocean leads to heavy isotope enrichment in preformed NO−3, while nitrate assimilation in low latitudes removes the δ18O signal of the preformed NO−3 and replaces it with the nitrification signal  between nitrification and assimilation to partition isotopes between the NO−3 and particulate N pools. One important difference from the Wankel et al.  and δ18O3NO should be raised (because the δ18O of newly produced NO−3 was higher than that removed). Both of these factors should lead to negative Δ values, as discussed above, but they found that nitrification had a relatively small impact on δ15N3NO and δ18O3NO values in the Polar Antarctic Zone. They concluded that in the Polar Antarctic Zone less than 1% of NO−3 assimilated by phytoplankton is likely to have been produced by nitrification in the euphotic zone . Because the δ15N2NO and δ18O2NO values produced from ammonia oxidation and nitrate reduction are distinct, the sources can be readily distinguished. Based on natural abundance δ15N2NO and δ18O2NO data, ammonia oxidation was inferred to be the main source of NO−2 to the PNM in the Arabian Sea.In another recent study of PNM dynamics, natural abundance δ2O produced during ammonia oxidation has hampered the interpretation of near-surface N2O production rates and fluxes using two-component end member models . Both ammonia and nitrite oxidation are involved with large and distinctive isotope effects, leading to predictable patterns in the isotope ratios of compounds that they transform. The discovery of AOA and their importance in ocean biogeochemistry necessitates renewed study of the isotopic systematics of nitrification. In preliminary studies, the isotopic systematics of AOA appear similar to AOB for N isotope fractionation and O atom incorporation into NO−2 (Santoro and Casciotti, 2O and the isotopic systematics of this process need to be further investigated.Understanding the nitrogen and oxygen isotopic systematics of nitrification can contribute greatly to our understanding of nitrogen cycling in the ocean, as nitrification is involved with transformations between the major pools of DIN (NHThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.