Determination of the stability constants of mercury(II) complexes of mixed donor macrobicyclic encapsulating ligands

The reactions of mercury(II) with the mixed donor encapsulating ligands 3,6,16-trithia-6,11,19-triazabicyclo[6.6.6]icosane (AMN(3)S(3)sar) and 1-amino-8-methyl-6,19-dithia-3,10,13,16-tetraazabicyclo[6.6.6]icosane (AMN(4)S(2)sar) have been studied. NMR ligand-ligand competition experiments with the ligands 1,4,8,11-tetraazaeyclotetradecane ([14]aneN(4)), 1-thia-4,7,10-triazacyclododecane ([12]aneN(3)S) and ethylenediaminetetraacetic acid (EDTA) with AMN(3)S(3)sar and Hg(II) indicated that [14]aneN(4) would be an appropriate competing ligand for the, determination of the Hg(II) stability constant. Calculations indicated the ratio of concentrations of AMN3S3sar, [14]aneN(4) and Hg(II) required for the determination of the stability constant ranged from 1:1:1 to 1:5:1. Refinement of the titration curves yielded log(10)K[Hg(AMN(3)S(3)sar)](2+) = 17.7. A similar competition titration resulted in the determination of the stability constant for the AMN(4)S(2)sar system as log(10)K[Hg(AMN(4)S(2)sar)](2+) = 19.5. The observed binding constants for the mixed N/S donor systems and the hexaaza analogues sar (3,6,10,13,16,19-hexaazabicyclo [6.6.6]icosane) and diamsar (1,8-diamino-3,6,10,13,16,19 -hexazabicyclo [6.6.6] icosane (log(10)K-[Hg(diamsar)](2+) = 26.4; log(10)K[Hg(sar)](2+) = 28.1) differ by approximately ten orders of magnitude. The difference is ascribed not to a cryptate effect but to a mismatch in the Hg-N and Hg-S bond lengths in the N/S systems.

Modeling the optical constants of solids using acceptance-probability-controlled simulated annealing with an adaptive move generation procedure

The acceptance-probability-controlled simulated annealing with an adaptive move generation procedure, an optimization technique derived from the simulated annealing algorithm, is presented. The adaptive move generation procedure was compared against the random move generation procedure on seven multiminima test functions, as well as on the synthetic data, resembling the optical constants of a metal. In all cases the algorithm proved to have faster convergence and superior escaping from local minima. This algorithm was then applied to fit the model dielectric function to data for platinum and aluminum.

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to and the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum. (C) 1997 Optical Society of America.

Value congruence in leader-member exchange

In this study, hypotheses were tested that the quality of leader-member exchanges (LMX) depends on congruity of values between leader and member. Data on negotiating latitude and personal values were gathered from 160 members of 30 work groups in Australian organizations. Factor analysis revealed 5 value dimensions: Freedom, Achievement, Mateship, Obedience, and Coping. Analyses of variance supported the hypothesis that LMX quality is higher when leaders and members share achievement and obedience values. Subsequent exploratory analysis, however, indicated that a more complex model based on compatibility of leader authority and member affiliation values may provide a more complete representation.

Structure-hepatic disposition relationships for cationic drugs in isolated perfused rat livers: Transmembrane exchange and cytoplasmic binding process

This work studied the structure-hepatic disposition relationships for cationic drugs of varying lipophilicity using a single-pass, in situ rat liver preparation. The lipophilicity among the cationic drugs studied in this work is in the following order: diltiazem. propranolol. labetalol. prazosin. antipyrine. atenolol. Parameters characterizing the hepatic distribution and elimination kinetics of the drugs were estimated using the multiple indicator dilution method. The kinetic model used to describe drug transport (the two-phase stochastic model) integrated cytoplasmic binding kinetics and belongs to the class of barrier-limited and space-distributed liver models. Hepatic extraction ratio (E) (0.30-0.92) increased with lipophilicity. The intracellular binding rate constant (k(on)) and the equilibrium amount ratios characterizing the slowly and rapidly equilibrating binding sites (K-S and K-R) increase with the lipophilicity of drug (k(on) : 0.05-0.35 s(-1); K-S : 0.61-16.67; K-R : 0.36-0.95), whereas the intracellular unbinding rate constant (k(off)) decreases with the lipophilicity of drug (0.081-0.021 s(-1)). The partition ratio of influx (k(in)) and efflux rate constant (k(out)), k(in)/k(out), increases with increasing pK(a) value of the drug [from 1.72 for antipyrine (pK(a) = 1.45) to 9.76 for propranolol (pK(a) = 9.45)], the differences in k(in/kout) for the different drugs mainly arising from ion trapping in the mitochondria and lysosomes. The value of intrinsic elimination clearance (CLint), permeation clearance (CLpT), and permeability-surface area product (PS) all increase with the lipophilicity of drug [CLint (ml . min(-1) . g(-1) of liver): 10.08-67.41; CLpT (ml . min(-1) . g(-1) of liver): 10.80-5.35; PS (ml . min(-1) . g(-1) of liver): 14.59-90.54]. It is concluded that cationic drug kinetics in the liver can be modeled using models that integrate the presence of cytoplasmic binding, a hepatocyte barrier, and a vascular transit density function.

The linker domain of the Ha-Ras hypervariable region regulates interactions with exchange factors, Raf-1 and phosphoinositide 3-kinase

Ha-Ras and Ki-Ras have different distributions across plasma membrane microdomains. The Ras C-terminal anchors are primarily responsible for membrane microlocalization, but recent work has shown that the interaction of Ha-Ras with lipid rafts is modulated by GTP loading via a mechanism that requires the hypervariable region (HVR). We have now identified two regions in the HVR linker domain that regulate Ha-Ras raft association. Release of activated Ha-Ras from lipid rafts is blocked by deleting amino acids 173-179 or 166-172. Alanine replacement of amino acids 173-179 but not 166-172 restores wild type micro-localization, indicating that specific N-terminal sequences of the linker domain operate in concert with a more C-terminal spacer domain to regulate Ha-Ras raft association. Mutations in the linker domain that confine activated Ha-RasG12V to lipid rafts abrogate Raf-1, phosphoinositide 3-kinase, and Akt activation and inhibit PC 12 cell differentiation. N-Myristoylation also prevents the release of activated Ha-Ras from lipid rafts and inhibits Raf-1 activation. These results demonstrate that the correct modulation of Ha-Ras lateral segregation is critical for downstream signaling. Mutations in the linker domain also suppress the dominant negative phenotype of Ha-RasS17N, indicating that HVR sequences are essential for efficient interaction of Ha-Ras with exchange factors in intact cells.

Brief note on the variation of constants formula for fuzzy differential equations

This note gives a theory of state transition matrices for linear systems of fuzzy differential equations. This is used to give a fuzzy version of the classical variation of constants formula. A simple example of a time-independent control system is used to illustrate the methods. While similar problems to the crisp case arise for time-dependent systems, in time-independent cases the calculations are elementary solutions of eigenvalue-eigenvector problems. In particular, for nonnegative or nonpositive matrices, the problems at each level set, can easily be solved in MATLAB to give the level sets of the fuzzy solution. (C) 2002 Elsevier Science B.V. All rights reserved.