We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat and Female Rat, with leave-one-out ( LOO) validation concordance values of 69%, 74%, 67% and 73%, respectively. The mutagenic carcinogens produced concordance values in the range 69-76% compared with only 47-53% for non-mutagenic carcinogens. As a surrogate for mutagenicity, comparisons between single site and multiple site carcinogen SAR models were analysed. The LOO concordance values for models consisting of 1-site, 2-site and 4+-site carcinogens were 66%, 71% and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted.

SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen-Non-Carcinogen (TSC-NC) and Target Site Carcinogen-Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a specific target site and inactive ones that were whole animal non-carcinogens. On the other hand, the TSC-NTSC models used an inactive category also composed of carcinogens but to any/all other sites but the target site. Leave one out (LOO) validations produced an overall average concordance value for all 12 models of 0.77 for the TSC-NC models and 0.73 for the TSC-NTSC models. Overall, these findings suggest that while the TSC-NC models are able to distinguish between carcinogens and non-carcinogens, the TSC-NTSC models are identifying structural attributes that associate carcinogens to specific tumour sites. Since the TSC-NTSC models are composed of active and inactive compounds that are genotoxic and non-genotoxic carcinogens, the TSC-NTSC models may be capable of deciphering non-genotoxic mechanisms of carcinogenesis. Together, models of this type may also prove useful in anticancer drug development since they essentially contain chemical moieties that target a specific tumour site.

Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor interactions of carcinogens, non-carcinogens, and mammary carcinogens to a set of 5494 target proteins. Leave-one-out validations of the ligand mammary carcinogen-non-carcinogen model had a concordance between experimental and predicted results of 71%, and the mammary carcinogen-non-mammary carcinogen model was 72% concordant. The development of a hybrid fragment-ligand model improved the concordances to 85 and 83%, respectively. In a separate external validation exercise, hybrid fragment-ligand models had concordances of 81 and 76%. Analyses of example rat mammary carcinogens including the food mutagen and oestrogenic compound PhIP, the herbicide atrazine, and the drug indomethacin; the ligand model identified a number of proteins associated with each compound that had previously been referenced in Medline in conjunction with the test chemical and separately with association to breast cancer. This new modelling approach can enhance model predictivity and help bridge the gap between chemical structure and carcinogenic activity by descriptors that are related to biological targets.

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.

A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure-activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17beta-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rate, respectively, for compounds not in the learning sets. The good predictive ability of these models and their basis on simple to understand 2-D molecular fragments indicates their potential usefulness in computational screening methods for environmental estrogens.

The National Cancer Institute's Developmental Therapeutics Program maintains the screening results obtained in 60 standardized cancer cell lines and contained 37,836 compounds for this study. This dataset has shown to be an outstanding resource for the development of structure-activity relationship (SAR) models describing anticancer activity. We report here a novel SAR modeling approach based on a subtractive protocol to develop models that describe cell type-specific molecular descriptors of cytotoxicity. The goal of this approach is to separate features associated with antiproliferative activity to many cell lines from those that effect only a specific cell type. To assess this approach, we developed SAR models for cytostatic activity against the human breast cancer cell lines MCF-7 and MDA-MB-231 and one differential activity model for compounds that were potent cytostatic agents in MCF-7 cells but relatively inactive against MDA-MB-231 cells. The models were between 72 and 84% accurate when challenged with compounds not in the learning sets. Structural features associated with the differential activity model highlighted how the use of this approach can selectively identify chemical moieties associated with potent cytostatic action to MCF-7 but not to MDA-MB-231 cells. We surmise that outgrowth of this method can facilitate the development of SAR models with sufficient resolution and clarity to identify chemical moieties associated with antiproliferative activity to selective individual cancer types while being innocuous to other cell types.

The Making Connections, Making Choices program is a multidisciplinary, neuroscience-focused project aimed at middle-school students and teachers primarily throughout Washington State and also across the country. The three components--the Summer Institute (for teacher training), the Brain Power Van (to visit schools and provide neuroscience education), and the speakers' bureau (to train clinicians and researchers to provide effective class-room and public talks and to schedule engagements)--work together to foster enriching, interactive science education experiences for students and teachers. The program has been funded by the National Center for Research Resources at the National Institutes of Health since 1991. Each year the aspect of it described in this article reaches 30-35 schools, with a total of more than 1,000 students and 80 teachers, plus another 30-40 teachers each summer. The program seeks to (1) enhance middle-school students' science knowledge, (2) help science teachers improve their science knowledge and teaching, (3) increase understanding and appreciation of biomedical research, (4) increase understanding of why animals are used in research, and (5) promote students' interest in science careers, especially the interest of students from groups underrepresented in science. Periodic evaluations showed that students exposed to the program scored higher on tests of neuroscience knowledge and had more interest in health science careers than did control groups of non-exposed students. The authors argue an important aspect of the program is that it has a broad focus and is multidisciplinary.

A model calculation is presented for the orbital density of states, as well as the change in the density of states due to the chemisorption, of a two-level adsorbate bonded to the s-band of an fcc(100) metal surface. The adsorption geometry assumes that the adatom is over the fourfold hole site, with a π-bonding interaction with the diagonal substrate atoms. The method used is the Green's function formalism, with the LCAO-tight-binding approximation. Even when the model assumes only one level interacts directly with the surface, the orbital resonances contain contributions from both adorbitals as well as from the substrate group orbital which participates in the bonding. The admixture of each orbital in the resonances can be understood qualitatively in terms of both direct and indirect interactions which depend on the parameters of the model, namely, the unperturbed adorbital energies, the adsorbate-substrate coupling strengths, and the intra-adsorbate coupling strength.

Contrary to previous theories, it is shown that the intensity of molecular vibrational energy loss peaks in Inelastic Electron Tunneling varies as n1.3 rather than as n, where n is the concentration of adsorbate molecules on the insulator surface in metal-insulator-metal junctions. Even though obtained with a simple model for the junction, this dependence agrees quantitatively with experimental results of Langan and Hansma.

A new method for determining the angles of incidence in a back-reflection, post-acceleration, fluorescent display LEED apparatus is presented which uses the angles between the diffraction spots on a photograph of the LEED pattern. Absolute accuracies better than 0.1 degrees for both incidence angles should be routinely available.

The previously developed soft-mode theory of surface reconstruction is applied to the (111) surface of silicon. It is shown that the mutual interaction of surface dynamic effective charges can cause the ideal equilibrium configuration of the surface atoms to be unstable, thus leading to reconstruction. This instability is studied in detail using lattice-dynamical methods and it is found that dynamic effective charges on the order of $0.5e$ can lead to the 2 \ifmmode\times\else\texttimes\fi{} 1 reconstruction observed experimentally. The directions in which the atoms displace due to the instability are shown and a new atomic configuration for the reconstructed surface is thereby predicted. The results are found to be nearly independent of the details of surface relaxation, of the distribution of the dynamic effective charges, and of the supposed weakening of the short-range forces in the surface region.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation E. D. Williams, S. L. Cunningham, W. H. Weinberg; Abstract: Determination of adatom interaction energies by a Monte Carlo calculation: Oxygen on W(110). J. Vac. Sci. Technol. 1 March 1978; 15 (2): 417–418. https://doi.org/10.1116/1.569584 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAVS: Science & Technology of Materials Interfaces and ProcessingJournal of Vacuum Science and Technology Search Advanced Search |Citation Search

A method for determining the atomic structure of a clean metal surface from low-energy-electron diffraction data is presented using the Fourier transform of the beam intensity as a function of electron momentum (the Patterson function). A model Patterson function which is made up of the convolution product of a window transform function and a series of $\ensuremath{\delta}$ functions representing the crystal layer spacing is fitted in a least-squares sense to the Patterson function of the data. By varying the positions of the $\ensuremath{\delta}$ functions and examining the quality of the fit, the structure can be determined. The method is fully illustrated using model kinematic data from a surface which is both relaxed and unrelaxed. The approximations necessary to obtain a perfect fit between the Patterson function of the kinematic data and the convolution product are discussed showing that even ideal data cannot be analyzed to give an "exact" solution. Nevertheless, it is shown that the method is capable of determining structure even from data that are highly dynamic. This is illustrated by analyzing multiple-scattering data for W(110) which have been calculated by Van Hove and Tong. Four nonequivalent beams are analyzed for inward relaxation, outward relaxation, and no relaxation. The effect of using data with different energy ranges is discussed as well as different prescriptions for choosing the window transform function.

A new formalism for obtaining the Green's functions suitable for studying the surface properties and chemisorption properties of real crystals is presented. The method has a number of calculational advantages which make it possible to examine the problem of chemisorption on the surface of crystals which are represented by realistic and complex band structures. The method is illustrated by applying it first to a linear monatomic chain, second to a simple cubic $s$-band crystal, and third to a two-band fcc crystal. The first two well-known examples are presented to verify the method. The last example has not been considered before and shows the importance of including in the model calculation all of the crystal bands, even those not involved directly in the chemisorption bond.

A Monte Carlo simulation has been carried out to describe two-dimensional order–disorder phenomena. The model contains (attractive) first, (repulsive) second, and (attractive) third neighbor pairwise interactions. The special case of oxygen chemisorption on a tungsten (110) surface, on which an ordered p (2×1) overlayer is formed at low surface temperatures, is considered explicitly. From the measured order–disorder transition temperatures at both quarter- and half-monolayer surface coverages, (nonunique) values of the three pairwise interaction energies have been determined. These pairwise interaction energies have been used to determine the variation in the total interaction energy, the heat capacity and the entropy with surface temperature.

An Ir(110)-(1 × 1) surface structure has been prepared by adsorbing 14 monolayer of oxygen at 850 K on a clean, reconstructed (1 × 2) surface. Results of the low-energy electron diffraction structure analysis reveal that the oxygen is probably distributed randomly over the crystal surface, and the (1 × 1) structure is the same as a clean unreconstructed (1 × 1) structure, with a topmost interlayer Ir spacing of 1.26 ± 0.05 Å. This is equivalent to a contraction of approximately 7.5% of the bulk interlayer spacing of 1.36 Å.

The adsorption both of a single atom and a monolayer of atoms on the (001) surface of a model two-band crystal with the CsCI structure is investigated using the Green's function formalism and the phase shift technique. The electronic structure of the surface is described within the Linear Combination of Atomic Orbitals (LCAO) scheme and the Tight Binding (TB) approximation. Each adatom is represented by a single non-degenerate energy level. The adatoms are placed on the surface in both the on-site and the centered fourfold-site configuration. The change in the density of electronic states upon chemisorption is found, and comparison is made with similar results on a metal surface. It is shown that many, but not all, of the qualitative features in chemisorption on metallic surfaces can be transferred to the case of an insulating surface. In addition, it is shown that there are systematic variations in the density of states with adatom coverage which depend upon the absorption site.