FLUORESCENT PROBE INVESTIGATIONS OF MICROENVIRONMENTS OF ANALYTICAL INTEREST (REVERSED-PHASE, POLYETHYLENIMINE, POLARIZATION)

Fluorescent probes were applied to investigate three systems. Reverse phase chromatographic surfaces were studied using ion pairs. Variables were cation reagent structure and concentration, bonded phase (methyl, octyl, octadecyl, and phenyl), and solvent (water or methanol). Emission wavelength shifts for the anionic polarity probe, ANS, (8-anilino-napthalene-1-sulfonate) reflect the nature and extent of lipophilic interactions. Tetramethylammonium promoted ANS penetration into surface structure. Tetrabutylammonium overcame aqueous surface alkyl aggregation, which greatly enhanced ANS-surface interaction of C18. For the other phases at high cation concentrations there was lipophilic interaction between ANS and cation. High concentrations of small cations excluded ANS from the surface, as did all levels of trimethylmyristylamine cation. Methanol solvation reduced lipophilic interactions with alkyl surfaces. Pi-pi interactions were important with the phenyl surface. Results are consistent with the ion interaction retention model for ion pairing chromatography, which is based on double layer formation on a dynamic surface. Polyelectrolyte-counterion binding strength and proximity were studied using a three component system: metals bound to polyethylenimine (PEI) and pyrenesulfonate counterion probes. Metals altered rates of excited state processes and defined binding environment. Variables were net charge on metal and probe and metal-amine complex properties. Cu(II)-PEI efficiently and selectively quenched probes. Ground state dimerization in Zn(II)-PEI implied territorial binding involving lipophilic interactions with PEI and between probes was important. Quenching and excimer formation in Ag(I)-PEI were due to more than net charge since protonated sites did not alter emission. Fluorescence polarization was used to detect intramolecular energy transfer between equivalent fluorophors in crown ethers, metal complexes, and simple organic molecules. Energy transfer randomizes the transition moment of emission relative to that of excitation, thereby decreasing polarization. In dilute glycerol solutions intermolecular depolarization is eliminated. A simple model based on Forster energy transfer theory was developed to distinguish molecules with different numbers of fluorophors and to obtain average angles between fluorophors, based on the extent of polarization differences

Mean field at distance one

To be able to understand how infectious diseases spread on networks, it is important to understand the network structure itself in the absence of infection. In this text we consider dynamic network models that are inspired by the (static) configuration network. The networks are described by population-level averages such as the fraction of the population with $k$ partners, $k=0,1,2,\ldots$ This means that the bookkeeping contains information about individuals and their partners, but no information about partners of partners. Can we average over the population to obtain information about partners of partners? The answer is `it depends', and this is where the mean field at distance one assumption comes into play. In this text we explain that, yes, we may average over the population (in the right way) in the static network. Moreover, we provide evidence in support of a positive answer for the network model that is dynamic due to partnership changes. If, however, we additionally allow for demographic changes, dependencies between partners arise. In earlier work we used the slogan `mean field at distance one' as a justification of simply ignoring the dependencies. Here we discuss the subtleties that come with the mean field at distance one assumption, especially when demography is involved. Particular attention is given to the accuracy of the approximation in the setting with demography. Next, the mean field at distance one assumption is discussed in the context of an infection superimposed on the network. We end with the conjecture that an extension of the bookkeeping leads to an exact description of the network structure.Comment: revised versio

Overshoot in biological systems modeled by Markov chains: a nonequilibrium dynamic phenomenon

A number of biological systems can be modeled by Markov chains. Recently, there has been an increasing concern about when biological systems modeled by Markov chains will perform a dynamic phenomenon called overshoot. In this article, we found that the steady-state behavior of the system will have a great effect on the occurrence of overshoot. We confirmed that overshoot in general cannot occur in systems which will finally approach an equilibrium steady state. We further classified overshoot into two types, named as simple overshoot and oscillating overshoot. We showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a nonequilibrium dynamic phenomenon with energy consumption. In addition, the main result in this article is validated with real experimental data.Comment: 15 pages, 3 figure

A neural blackboard architecture of sentence structure

We present a neural architecture for sentence representation. Sentences are represented in terms of word representations as constituents. A word representation consists of a neural assembly distributed over the brain. Sentence representation does not result from associations between neural word assemblies. Instead, word assemblies are embedded in a neural architecture, in which the structural (thematic) relations between words can be represented. Arbitrary thematic relations between arguments and verbs can be represented. Arguments can consist of nouns and phrases, as in sentences with relative clauses. A number of sentences can be stored simultaneously in this architecture. We simulate how probe questions about thematic relations can be answered. We discuss how differences in sentence complexity, such as the difference between subject-extracted versus object-extracted relative clauses and the difference between right-branching versus center-embedded structures, can be related to the underlying neural dynamics of the model. Finally, we illustrate how memory capacity for sentence representation can be related to the nature of reverberating neural activity, which is used to store information temporarily in this architecture