Accelerating seismic energy release and evolution of event time and size statistics: Results from two heterogeneous cellular automaton models

The evolution of event time and size statistics in two heterogeneous cellular automaton models of earthquake behavior are studied and compared to the evolution of these quantities during observed periods of accelerating seismic energy release Drier to large earthquakes. The two automata have different nearest neighbor laws, one of which produces self-organized critical (SOC) behavior (PSD model) and the other which produces quasi-periodic large events (crack model). In the PSD model periods of accelerating energy release before large events are rare. In the crack model, many large events are preceded by periods of accelerating energy release. When compared to randomized event catalogs, accelerating energy release before large events occurs more often than random in the crack model but less often than random in the PSD model; it is easier to tell the crack and PSD model results apart from each other than to tell either model apart from a random catalog. The evolution of event sizes during the accelerating energy release sequences in all models is compared to that of observed sequences. The accelerating energy release sequences in the crack model consist of an increase in the rate of events of all sizes, consistent with observations from a small number of natural cases, however inconsistent with a larger number of cases in which there is an increase in the rate of only moderate-sized events. On average, no increase in the rate of events of any size is seen before large events in the PSD model.

Experimental methods for studying drug uptake in the head and brain

A number of techniques have been developed to study the disposition of drugs in the head and, in particular, the role of the blood-brain barrier (BBB) in drug uptake. The techniques can be divided into three groups: in-vitro, in-vivo and in-situ. The most suitable method depends on the purpose(s) and requirements of the particular study being conducted. In-vitro techniques involve the isolation of cerebral endothelial cells so that direct investigations of these cells can be carried out. The most recent preparations are able to maintain structural and functional characteristics of the BBB by simultaneously culturing endothelial cells with astrocytic cells,The main advantages of the in-vitro methods are the elimination of anaesthetics and surgery. In-vivo methods consist of a diverse range of techniques and include the traditional Brain Uptake Index and indicator diffusion methods, as well as microdialysis and positron emission tomography. In-vivo methods maintain the cells and vasculature of an organ in their normal physiological states and anatomical position within the animal. However, the shortcomings include renal acid hepatic elimination of solutes as well as the inability to control blood flow. In-situ techniques, including the perfused head, are more technically demanding. However, these models have the ability to vary the composition and flow rate of the artificial perfusate. This review is intended as a guide for selecting the most appropriate method for studying drug uptake in the brain.

Free energy approximations in simple lattice proteins

This work addresses the question of whether it is possible to define simple pairwise interaction terms to approximate free energies of proteins or polymers. Rather than ask how reliable a potential of mean force is, one can ask how reliable it could possibly be. In a two-dimensional, infinite lattice model system one can calculate exact free energies by exhaustive enumeration. A series of approximations were fitted to exact results to assess the feasibility and utility of pairwise free energy terms. Approximating the true free energy with pairwise interactions gives a poor fit with little transferability between systems of different size. Adding extra artificial terms to the approximation yields better fits, but does not improve the ability to generalize from one system size to another. Furthermore, one cannot distinguish folding from nonfolding sequences via the approximated free energies. Most usefully, the methodology shows how one can assess the utility of various terms in lattice protein/polymer models. (C) 2001 American Institute of Physics.

Th structure of the P-II-ATP complex

P-II is a signal transduction protein that is part of the cellular machinery used by many bacteria to regulate the activity of glutamine synthetase and the transcription of its gene. The structure of P-II was solved using a hexagonal crystal form (form I). The more physiologically relevant form of P-II is a complex with small molecule effecters. We describe the structure of P-II with ATP obtained by analysis of two different crystal forms (forms II and III) that were obtained by co-crystallization of P-II with ATP. Both structures have a disordered recognition (T) loop and show differences at their C termini. Comparison of these structures with the form I protein reveals changes that occur on binding ATP. Surprisingly, the structure of the P-II/ATP complex differs with that of GlnK, a functional homologue. The two proteins bind the base and sugar of ATP in a similar manner but show differences in the way that they interact with the phosphates. The differences in structure could account for the differences in their activities, and these have been attributed to a difference in sequence at position 82. It has been demonstrated recently that P-II and GlnK form functional heterotrimers in vivo. We construct models of the heterotrimers and examine the junction between the subunits.

Modelling solute transport in structured soils: Performance evaluation of the ADR and TRM models

The movement of chemicals through the soil to the groundwater or discharged to surface waters represents a degradation of these resources. In many cases, serious human and stock health implications are associated with this form of pollution. The chemicals of interest include nutrients, pesticides, salts, and industrial wastes. Recent studies have shown that current models and methods do not adequately describe the leaching of nutrients through soil, often underestimating the risk of groundwater contamination by surface-applied chemicals, and overestimating the concentration of resident solutes. This inaccuracy results primarily from ignoring soil structure and nonequilibrium between soil constituents, water, and solutes. A multiple sample percolation system (MSPS), consisting of 25 individual collection wells, was constructed to study the effects of localized soil heterogeneities on the transport of nutrients (NO3-, Cl-, PO43-) in the vadose zone of an agricultural soil predominantly dominated by clay. Very significant variations in drainage patterns across a small spatial scale were observed tone-way ANOVA, p < 0.001) indicating considerable heterogeneity in water flow patterns and nutrient leaching. Using data collected from the multiple sample percolation experiments, this paper compares the performance of two mathematical models for predicting solute transport, the advective-dispersion model with a reaction term (ADR), and a two-region preferential flow model (TRM) suitable for modelling nonequilibrium transport. These results have implications for modelling solute transport and predicting nutrient loading on a larger scale. (C) 2001 Elsevier Science Ltd. All rights reserved.

Aluminophosphate-based mesoporous molecular sieves: synthesis and characterization of TAPOs

Mesoporous Ti-substituted aluminophosphates (AlPOs) with a hexagonal, cubic and lamellar pore structure, characteristic of MCM-41, MCM-48. and MCM-50, respectively, were synthesized. The stability of these mesophases upon template removal was studied. The pore structures, surface properties, and local atom environments of Al, P, and Ti of the hexagonal and cubic Ti-containing mesoporous products were extensively characterized using X-ray diffraction, magic angle spinning nuclear magnetic resonance, AAS, XPS, ultraviolet-visible, and adsorption of nitrogen and water vapor techniques while the lamellar mesophase was not further characterized due to its very poor thermal stability. Ti-containing mesoporous AlPO materials show a reasonable thermal stability upon template removal, a hydrophilic surface property, and high porosity showing application potentials in catalytic oxidation of hydrocarbons. (C) 2001 Elsevier Science B,V. All rights reserved.

Charge ordering and antiferromagnetic exchange in layered molecular crystals of the theta type

We consider the electronic properties of layered molecular crystals of the type theta -D(2)A where A is an anion and D is a donor molecule such as bis-(ethylenedithia-tetrathiafulvalene) (BEDT-TTF), which is arranged in the theta -type pattern within the layers. We argue that the simplest strongly correlated electron model that can describe the rich phase diagram of these materials is the extended Hubbard model on the square lattice at one-quarter filling. In the limit where the Coulomb repulsion on a single site is large, the nearest-neighbor Coulomb repulsion V plays a crucial role. When V is much larger than the intermolecular hopping integral t the ground state is an insulator with charge ordering. In this phase antiferromagnetism arises due to a novel fourth-order superexchange process around a plaquette on the square lattice. We argue that the charge ordered phase is destroyed below a critical nonzero value V, of the order of t. Slave-boson theory is used to explicitly demonstrate this for the SU(N) generalization of the model, in the large-N limit. We also discuss the relevance of the model to the all-organic family beta-(BEDT-TTF)(2)SF5YSO3 where Y=CH2CF2, CH2, CHF.

Enzymology and molecular biology of prokaryotic sulfite oxidation

Despite its toxicity, sulfite plays a key role in oxidative sulfur metabolism and there are even some microorganisms which can use it as sole electron source. Sulfite is the main intermediate in the oxidation of sulfur compounds to sulfate, the major product of most dissimilatory sulfur-oxidizing prokaryotes. Two pathways of sulfite oxidation are known: (1) direct oxidation to sulfate catalyzed by a sulfite: acceptor oxidoreductase, which is thought to be a molybdenum-containing enzyme; (2) indirect oxidation under the involvement of the enzymes adenylylsulfate (APS) reductase and ATP sulfurylase and/or adenylylsulfate phosphate adenylyltransferase with APS as an intermediate. The latter pathway allows substrate phosphorylation and occurs in the bacterial cytoplasm. Direct oxidation appears to have a wider distribution; however, a redundancy of pathways has been described for diverse photo- or chemotrophic, sulfite-oxidizing prokaryotes. In many pro- and also eukaryotes sulfite is formed as a degradative product from molecules containing sulfur as a heteroatom. In these organisms detoxification of sulfite is generally achieved by direct oxidation to sulfate. (C) 2001 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Integrable Kondo impurity in one-dimensional q-deformed t-J models

Integrable Kondo impurities in two cases of one-dimensional q-deformed t-J models are studied by means of the boundary Z(2)-graded quantum inverse scattering method. The boundary K matrices depending on the local magnetic moments of the impurities are presented as nontrivial realizations of the reflection equation algebras in an impurity Hilbert space. Furthermore, these models are solved by using the algebraic Bethe ansatz method and the Bethe ansatz equations are obtained.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.

Clinical and molecular genetics of myoclonic-astatic epilepsy and severe myoclonic epilepsy in infancy (Dravet syndrome)

The majority of severe epileptic encephalopathies of early childhood are symptomatic where a clear etiology is apparent. There is a small subgroup, however, where no etiology is found on imaging and metabolic studies, and genetic factors are important. Myoclonic-astatic epilepsy (MAE) and severe myoclonic epilepsy in infancy (SMEI), also known as Dravet syndrome, are epileptic encephalopathies where multiple seizure types begin in the first few years of life associated with developmental slowing. Clinical and molecular genetic studies of the families of probands with MAE and SMEI suggest a genetic basis. MAE was originally identified as part of the genetic epilepsy syndrome generalized epilepsy with febrile seizures plus (GEFS(+)). Recent clinical genetic studies suggest that SMEI forms the most severe end of the spectrum of the GEFS(+). GEF(+) has now been associated with molecular defects in three sodium channel subunit genes and a GABA subunit gene. Molecular defects of these genes have been identified in patients with MAE and SMEI. Interestingly, the molecular defects in MAE have been found in the setting of large GEFS(+) pedigrees, whereas, more severe truncation mutations arising de novo have been identified in patients with SMEI. It is likely that future molecular studies will shed light on the interaction of a number of genes, possibly related to the same or different ion channels, which result in a severe phenotype such as MAE and SMEI. (C) 2001 Elsevier Science B.V. All rights reserved.

Porous clays and pillared clays-based catalysts. Part 2: A review of the catalytic and molecular sieve applications

Metal oxide pillared clay (PILC) possesses several interesting properties, such as large surface area, high pore volume and tunable pore size (from micropore to mesopore), high thermal stability, strong surface acidity and catalytic active substrates/metal oxide pillars. These unique characteristics make PILC an attractive material in catalytic reactions. It can be made either as catalyst support or directly used as catalyst. This paper is a continuous work from Kloprogge's review (J.T. Kloprogge, J. Porous Mater. 5, 5 1998) on the synthesis and properties of smectites and related PILCs and will focus on the diverse applications of clay pillared with different types of metal oxides in the heterogeneous catalysis area and adsorption area. The relation between the performance of the PILC and its physico-chemical features will be addressed.

Mathematical models in percutaneous absorption

A number of mathematical models have been used to describe percutaneous absorption kinetics. In general, most of these models have used either diffusion-based or compartmental equations. The object of any mathematical model is to a) be able to represent the processes associated with absorption accurately, b) be able to describe/summarize experimental data with parametric equations or moments, and c) predict kinetics under varying conditions. However, in describing the processes involved, some developed models often suffer from being of too complex a form to be practically useful. In this chapter, we attempt to approach the issue of mathematical modeling in percutaneous absorption from four perspectives. These are to a) describe simple practical models, b) provide an overview of the more complex models, c) summarize some of the more important/useful models used to date, and d) examine sonic practical applications of the models. The range of processes involved in percutaneous absorption and considered in developing the mathematical models in this chapter is shown in Fig. 1. We initially address in vitro skin diffusion models and consider a) constant donor concentration and receptor conditions, b) the corresponding flux, donor, skin, and receptor amount-time profiles for solutions, and c) amount- and flux-time profiles when the donor phase is removed. More complex issues, such as finite-volume donor phase, finite-volume receptor phase, the presence of an efflux. rate constant at the membrane-receptor interphase, and two-layer diffusion, are then considered. We then look at specific models and issues concerned with a) release from topical products, b) use of compartmental models as alternatives to diffusion models, c) concentration-dependent absorption, d) modeling of skin metabolism, e) role of solute-skin-vehicle interactions, f) effects of vehicle loss, a) shunt transport, and h) in vivo diffusion, compartmental, physiological, and deconvolution models. We conclude by examining topics such as a) deep tissue penetration, b) pharmacodynamics, c) iontophoresis, d) sonophoresis, and e) pitfalls in modeling.

A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A

Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates. Copyright (C) 2002 John Wiley Sons, Ltd.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.