Effect of Particle-Hole Asymmetry on the Mott-Hubbard Metal-Insulator Transition

The Mott-Hubbard metal-insulator transition is one of the most important problems in correlated electron systems. In the past decade, much progress has been made on examining a particle-hole symmetric form of the transition in the Hubbard model with dynamical mean field theory where it was found that the electronic self energy develops a pole at the transition. We examine the particle-hole asymmetric metal-insulator transition in the Falicov-Kimball model, and find that a number of features change when the noninteracting density of states has a finite bandwidth. Since, generically particle-hole symmetry is broken in real materials, our results have an impact on understanding the metal-insulator transition in real materials.Comment: 5 pages, 3 figure

Synthesis of N-Benzyl-N-Methyl-β-Chloro-β(p-Chlorophenyl)-Ethylamine-Hydrochloride

Classes of compounds often have similar characteristics and activities. However, it has been showin in pharmacological studies that a small change in a structure produced by a substitution of an atom or a group of atoms on the original compound may cause a marked change in biological activity. When a certain compound shows usefulness as a drug, many variations of this compound are studied to see if a more useful form can be made. Two characteristics looked for are (1) effectiveness, which includes the onset or how long before the drug takes effect, and the duration or how long the effect lasts, and (2) lack of toxicity

HEPATIC ORNITHINE METABOLISM IN HYDRAZINE-TREATED RATS

Ornithine plays an important role in mammalian intermediary metabolism. Ornithine can be (1) utilized as a component of the urea cycle, (2) decarboxylated to form putrescine, a precursor of the polyamines, spermidine and spermine, and (3) converted metabolically to arginine, proline and glutamate. In prior investigations conducted in our laboratories and others, free endogenous hepatic pools of ornithine were found to be elevated following hydrazine administration. Consequently, time-course alterations of arginase, ornithine δ-transaminase, ornithine transcarbamylase, and ornithine decarboxylase activities were investigated regarding the effect of hydrazine treatment on hepatic ornithine metabolism in the rat. As an outgrowth of these investigations, a time-course study of the hepatic concentration of putrescine, spermidine and spermine was warranted. Male albino rats (Holtzman) were injected with neutralized hydrazine (40 mg/kg, body weight, ip) or isotonic saline (1.0 ml/kg, body weight, ip) and fasted for various times. The effects ascribed to hydrazine treatment were based on the comparison of results from hydrazine-treated animals with those obtained from the saline-injected control animals. Maximal elevations of endogenous hepatic ornithine pool sizes were observed at 12 hr in supernatant preparations (1000% of control) and at 24 hr in homogenate preparations (600% of control). Hepatic arginase activity was found to reach its nadir at 4 hr (70% of control). Thus, arginase did not appear to be responsible for the increased ornithine levels resulting from hydrazine treatment. Hepatic ornithine δ-transaminase activity was decreased (40% of control) at 4 hr and this level of activity was sustained throughout the 24 hr period examined. Hepatic ornithine transcarbamylase activity was shown to be decreased maximally at 12 hr (40% of control). The decreased activities of these latter two enzymes offers a plausible explanation for the increased hepatic ornithine levels following hydrazine treatment. Although a remarkable increase (1500% of control) in ornithine decarboxylase activity was observed at 4 hr, it was not thought to influence significantly the levels of hepatic ornithine since the relative activities of the other enzymes involved in ornithine metabolism have been shown by others to be considerably greater than ornithine decarboxylase. This increase in ornithine decarboxylase activity was followed by sequential elevations in total endogenous hepatic putrescine and then spermidine levels. The total endogenous hepatic spermine levels were not altered except for a slight decrease at 48 hr. Regenerating liver following partial hepatectomy demonstrated alterations similar in magnitude, but with a different time-course in the changes in these parameters. These similarities along with other biochemical and morphological alterations established by others following either partial hepatectomy or hydrazine treatment suggest that the latter may be tantamount to a chemical partial hepatectomy

A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology

The widespread availability of high-dimensional biological data has made the simultaneous screening of numerous biological characteristics a central statistical problem in computational biology. While the dimensionality of such datasets continues to increase, the problem of teasing out the effects of biomarkers in studies measuring baseline confounders while avoiding model misspecification remains only partially addressed. Efficient estimators constructed from data adaptive estimates of the data-generating distribution provide an avenue for avoiding model misspecification; however, in the context of high-dimensional problems requiring simultaneous estimation of numerous parameters, standard variance estimators have proven unstable, resulting in unreliable Type-I error control under standard multiple testing corrections. We present the formulation of a general approach for applying empirical Bayes shrinkage approaches to asymptotically linear estimators of parameters defined in the nonparametric model. The proposal applies existing shrinkage estimators to the estimated variance of the influence function, allowing for increased inferential stability in high-dimensional settings. A methodology for nonparametric variable importance analysis for use with high-dimensional biological datasets with modest sample sizes is introduced and the proposed technique is demonstrated to be robust in small samples even when relying on data adaptive estimators that eschew parametric forms. Use of the proposed variance moderation strategy in constructing stabilized variable importance measures of biomarkers is demonstrated by application to an observational study of occupational exposure. The result is a data adaptive approach for robustly uncovering stable associations in high-dimensional data with limited sample sizes

Alien Registration- Van Dine, Hubbard H. (Medway, Penobscot County)

https://digitalmaine.com/alien_docs/8090/thumbnail.jp

Equation of motion approach to the Hubbard model in infinite dimensions

We consider the Hubbard model on the infinite-dimensional Bethe lattice and construct a systematic series of self-consistent approximations to the one-particle Green's function, $G^{(n)}(\omega),\ n=2,3,\dots\$ . The first $n-1$ equations of motion are exactly fullfilled by $G^{(n)}(\omega)$ and the $n$'th equation of motion is decoupled following a simple set of decoupling rules. $G^{(2)}(\omega)$ corresponds to the Hubbard-III approximation. We present analytic and numerical results for the Mott-Hubbard transition at half filling for $n=2,3,4$.Comment: 10pager, REVTEX, 8-figures not available in postscript, manuscript may be understood without figure

Antiferromagnetic gap in the Hubbard model

We compute the temperature dependence of the antiferromagnetic order parameter and the gap in the two dimensional Hubbard model at and close to half filling. Our approach is based on truncations of an exact functional renormalization group equation. The explicit use of composite bosonic degrees of freedom permits a direct investigation of the ordered low temperature phase. We show that the Mermin--Wagner theorem is not practically applicable for the spontaneous breaking of the continuous spin symmetry in the antiferromagnetic state. The critical behavior is dominated by the fluctuations of composite Goldstone bosons.Comment: new discussion of critical behavior 4 pages,2 figures, LaTe

Population Intervention Models in Causal Inference

Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a] treatment variable or risk variable on the distribution of a disease in a population. These models, as originally introduced by Robins (e.g., Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates, and its dependence on treatment. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject\u27s treatment and covariate history are measured over time, and an outcome is recorded at a final time point. In addition to the simpler, weighted regression approaches (inverse probability of treatment weighted estimators), more general (and robust) estimators have been developed and studied in detail for standard MSM (Robins (2000b), Neugebauer and van der Laan (2004), Yu and van der Laan (2003), van der Laan and Robins (2002)). In this paper we argue that in many applications one is interested in modeling the difference between a treatment-specific counterfactual population distribution and the actual population distribution of the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population, and therefore we refer to these models as intervention models. We focus on intervention models estimating the effect on an intervention in terms of a difference of means, ratio in means (e.g., relative risk if the outcome is binary), a so called switch relative risk for binary outcomes, and difference in entire distributions as measured by the quantile-quantile function. In addition, we provide a class of inverse probability of treatment weighed estimators, and double robust estimators of the causal parameters in these models. We illustrate the finite sample performance of these new estimators in a simulation study

Nonparametric population average models: deriving the form of approximate population average models estimated using generalized estimating equations

For estimating regressions for repeated measures outcome data, a popular choice is the population average models estimated by generalized estimating equations (GEE). We review in this report the derivation of the robust inference (sandwich-type estimator of the standard error). In addition, we present formally how the approximation of a misspecified working population average model relates to the true model and in turn how to interpret the results of such a misspecified model