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  • Articles  (117)
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  • 1
    Electronic Resource
    Electronic Resource
    Ferromagnetic nanocomposite films of cobalt in a ceramic matrix formed by thermal decomposition of cobalt nitride, CoN, precursor (1996)
    [S.l.] : American Institute of Physics (AIP)
    Journal of Applied Physics 79 (1996), S. 7905-7910 
    Details
    ISSN: 1089-7550
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Cobalt nitride films, CoN, in a pure form and also as a nanocomposite in boron nitride or silicon nitride were generated by reactive sputtering of cobalt metal, cobalt boride, or cobalt silicide as targets, respectively, in a nitrogen plasma. Cobalt nitride decomposes into the elements by heating under vacuum at 500 °C. The nanostructure of the composites was preserved in the heating treatment thus creating a fine dispersion (〈10 nm) of cobalt particles, in a ceramic matrix. The magnetic properties of the nanocomposites were established. The precursor cobalt nitride is paramagnetic while the cobalt dispersions, having dimensions smaller than single magnetic domain, show characteristics typical of those systems such as superparamagnetism and, at temperatures lower than the blocking temperature, marked hysteresis. The coercive fields at 5 K for the BN and Si3N4 nanocomposites are 3250 and 850 Oe, respectively. These films are of interest as data recording media.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.362402
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  • 2
    Electronic Resource
    Electronic Resource
    Thermal conversion of an iron nitride–silicon nitride precursor into a ferromagnetic nanocomposite (1998)
    [S.l.] : American Institute of Physics (AIP)
    Journal of Applied Physics 83 (1998), S. 905-910 
    Details
    ISSN: 1089-7550
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Iron nitride films, FeN, in a pure form and in the form of a nanocomposite in silicon nitride were prepared by reactive sputtering using iron or iron disilicide, respectively, as targets in a nitrogen plasma. Iron nitride decomposes into the elements by heating in vacuum to 800 °C. Intermediate phases such as Fe2N or Fe4N form at lower temperatures. The nanocomposites contain the iron phases as particles with an average size of ∼5 nm dispersed in the amorphous silicon nitride matrix. The magnetic properties of the nanocomposites were established. The precursor FeN–Si3N4 film is paramagnetic, while the Fe–Si3N4, obtained by heating in vacuum, is ferromagnetic and shows typical superparamagnetic behavior. These films are of interest as recording media with superior chemical and mechanical stability and may be encoded by localized heating.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.366776
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  • 3
    Electronic Resource
    Electronic Resource
    Characterization of sputtered amorphous platinum dioxide films (1998)
    [S.l.] : American Institute of Physics (AIP)
    Journal of Applied Physics 84 (1998), S. 6382-6386 
    Details
    ISSN: 1089-7550
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Amorphous platinum dioxide, a-PtO2, films are formed commonly during reactive sputtering of platinum at relatively high power density levels and high oxygen partial pressures. The structure of a-PtO2 is intermediate between the crystalline alpha and beta phases of this compound and either phase may form upon annealing or by lowering the power density during sputtering. Amorphous platinum dioxide is a semiconductor, and its resistivity depends on deposition parameters. Films of a-PtO2 are dense, chemically resistant, smooth, reflective, and have a hardness similar to titanium nitride. The films may be reduced in hydrogen at room temperature or in carbon monoxide at 200 °C to produce metallic platinum with crystallite sizes in the range of 5–10 nm. Any of these properties may be exploited to produce films that could be used in the development of sensors, optical materials, and in microelectronics. © 1998 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.368883
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  • 4
    Electronic Resource
    Electronic Resource
    Localized heating of nickel nitride/aluminum nitride nanocomposite films for data storage (1995)
    Woodbury, NY : American Institute of Physics (AIP)
    Applied Physics Letters 67 (1995), S. 3034-3036 
    Details
    ISSN: 1077-3118
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Nickel–aluminum nitride films were prepared by reactive sputtering of a nickel aluminide plate in a nitrogen plasma. The initial product is a nanocomposite containing the nickel as the nitride, Ni3N, in aluminum nitride. Heating in vacuum to 500 °C causes selective decomposition of the thermally labile nickel nitride leaving the aluminum nitride unaffected. The nickel nanocomposite is of interest for potential applications as recording media, as are other finely divided dispersions of ferromagnetic metals in insulating matrices. The nickel–aluminum nitride nanocomposite shows a moderate coercive field of 35 Oe at 300 K and, in common with ultrafine particles of ferromagnetic materials, shows superparamagnetic behavior. The Ni3N/AlN nanocomposite was subjected to localized heating with the focused beam of an argon-ion laser; this created features several microns in width that could be imaged with a magnetic force microscope, thus confirming its potential as a high density data storage medium. © 1995 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.115445
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  • 5
    Electronic Resource
    Electronic Resource
    A mixed-valence titanium(III)-titanium(IV) tetramer and related compounds (1986)
    s.l. : American Chemical Society
    Inorganic chemistry 25 (1986), S. 4213-4217 
    Details
    ISSN: 1520-510X
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/ic00243a030
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  • 6
    Electronic Resource
    Electronic Resource
    History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens (2005)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 1 (2005), S. 4 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment. These models, introduced by Robins, model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at a final time point. However, the utility of these models for some applications has been limited by their inability to incorporate modification of the causal effect of treatment by time-varying covariates. Particularly in the context of clinical decision making, such time-varying effect modifiers are often of considerable or even primary interest, as they are used in practice to guide treatment decisions for an individual. In this article we propose a generalization of marginal structural models, which we call history-adjusted marginal structural models (HA-MSM). These models allow estimation of adjusted causal effects of treatment, given the observed past, and are therefore more suitable for making treatment decisions at the individual level and for identification of time-dependent effect modifiers. Specifically, a HA-MSM models the conditional distribution of treatment-specific counterfactual outcomes, conditional on the whole or a subset of the observed past up till a time-point, simultaneously for all time-points. Double robust inverse probability of treatment weighted estimators have been developed and studied in detail for standard MSM. We extend these results by proposing a class of double robust inverse probability of treatment weighted estimators for the unknown parameters of the HA-MSM. In addition, we show that HA-MSM provide a natural approach to identifying the dynamic treatment regimen which follows, at each time-point, the history-adjusted (up till the most recent time point) optimal static treatment regimen. We illustrate our results using an example drawn from the treatment of HIV infection.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol1/iss1/4
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  • 7
    Electronic Resource
    Electronic Resource
    Super Learning: An Application to the Prediction of HIV-1 Drug Resistance (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    Statistical applications in genetics and molecular biology 6.2007, 1, art7 
    Details
    ISSN: 1544-6115
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology
    Notes: Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/sagmb/vol6/iss1/art7
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  • 8
    Electronic Resource
    Electronic Resource
    Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 3 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Marginal structural models (MSM) are an important class of models in causal inference. Given a longitudinal data structure observed on a sample of n independent and identically distributed experimental units, MSM model the counterfactual outcome distribution corresponding with a static treatment intervention, conditional on user-supplied baseline covariates. Identification of a static treatment regimen-specific outcome distribution based on observational data requires, beyond the standard sequential randomization assumption, the assumption that each experimental unit has positive probability of following the static treatment regimen. The latter assumption is called the experimental treatment assignment (ETA) assumption, and is parameter-specific. In many studies the ETA is violated because some of the static treatment interventions to be compared cannot be followed by all experimental units, due either to baseline characteristics or to the occurrence of certain events over time. For example, the development of adverse effects or contraindications can force a subject to stop an assigned treatment regimen.In this article we propose causal effect models for a user-supplied set of realistic individualized treatment rules. Realistic individualized treatment rules are defined as treatment rules which always map into the set of possible treatment options. Thus, causal effect models for realistic treatment rules do not rely on the ETA assumption and are fully identifiable from the data. Further, these models can be chosen to generalize marginal structural models for static treatment interventions. The estimating function methodology of Robins and Rotnitzky (1992) (analogue to its application in Murphy, et. al. (2001) for a single treatment rule) provides us with the corresponding locally efficient double robust inverse probability of treatment weighted estimator. In addition, we define causal effect models for "intention-to-treat" regimens. The proposed intention-to-treat interventions enforce a static intervention until the time point at which the next treatment does not belong to the set of possible treatment options, at which point the intervention is stopped. We provide locally efficient estimators of such intention-to-treat causal effects.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/3
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  • 9
    Electronic Resource
    Electronic Resource
    Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 6 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/6
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  • 10
    Electronic Resource
    Electronic Resource
    Ammonolysis of niobium(V) bromide (1987)
    s.l. : American Chemical Society
    Inorganic chemistry 26 (1987), S. 1459-1462 
    Details
    ISSN: 1520-510X
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/ic00256a030
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