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Raman spectra are obtained for the multilayer dispersion of rac-1,2,dioctadec-9'-cis-enyl-glycero-3-phosphorylcholine (dietherlecithin) in excess water. The CH stretching region was studied as a function of temperature and indicates that the multilayer dispersions undergo a liquid crystal to the gel phase transition at--21 +/- 4 degrees C.
Lipids that are covalently labeled with the 7-nitrobenz-2-oxa-1,3-diazol-4-yl (NBD) group are widely used as fluorescent analogues of native lipids in model and biological membranes to study a variety of processes. The fluorescent NBD group may be attached either to the polar or the apolar regions of a wide variety of lipid molecules. Synthetic routes for preparing the lipids, and spectroscopic and ionization properties of these probes are reviewed in this report. The orientation of various NBD-labeled lipids in membranes, as indicated by the location of the NBD group, is also discussed. The NBD group is uncharged at neutral pH in membranes, but loops up to the surface if attached to acyl chains of phospholipids. These lipids find applications in a variety of membrane-related studies which include membrane fusion, lipid motion and dynamics, organization of lipids and proteins in membranes, intracellular lipid transfer, and bilayer to hexagonal phase transition in liposomes. Use of NBD-labeled lipids as analogues of natural lipids is critically evaluated.
In this paper we study the transient behavior of a class of nonlinear differential systems verifying sign conditions through the succession of extrema of the state variables. This analysis does not depend, for the main part, on the analytical formulation of the model. The possible scenarios of sequences for the extrema, are represented on a graph and can be compared with the experimental data to validate the model. An application to the Droop model illustrates this method; we obtain as a result the global stability of the equilibrium and the possible successions of the extrema.
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MARCH-INSIDE methodology was applied to the prediction of the bitter tasting threshold of 48 dipeptides by means of pattern recognition techniques, in this case linear discriminant analysis (LDA), and regression methods. The LDA models yielded a percentage of good classification higher than 80% with the two main families of descriptor generated by this methodology (95.8% for self return probability and 83.3% using electronic delocalization entropy). The regression models can explain more than 80% of the experimental variance of the independent variable. Two regression models were obtained with R(2) values of 0.82 and 0.88 for the whole data and the data without two outliers, respectively; having a standard deviation of 0.27 and 0.23. The predictive power of the obtained equations was assessed by the Leave-One-Out cross validation procedures, giving the same percentages of good classification as in the training set, in the LDA models, and yielding values of q(2) of 0.78 and 0.86 in the regression model, respectively. The validation of this methodology was also carried out by comparison with previous reports modeling this data with other well-known methodologies, even 3-D molecular descriptors.
Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( approximately 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs.
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Ecologists are struggling to explain how so many tropical tree species can coexist in tropical forests, and several empirical studies have demonstrated that negative density dependence is an important mechanism of tree-species coexistence. Volkov et al. compare a model incorporating negative density dependence with a dispersal-limited neutral model and claim that each predicts six empirical species-abundance distributions of tropical-tree communities equally well. However, we show here that their main conclusion is premature: when the two models are compared in an improved analysis, we find that the dispersal-limited model outcompetes the density-dependent model in all six cases. Hence, although density dependence is certainly an important diversity-maintaining mechanism, our improved approach indicates that the dispersal-limited model provides a more parsimonious explanation of empirical species-abundance distributions.
This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.
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To study the etiology and explore possibilities for the therapy of implant-associated infections, investigators have developed and utilized various in vitro models. Major contributions have come from the non-oral medical field, where device-related infections can create life-threatening situations. Microbiological models may include (i) models to study the reaction of micro-organisms to the presence of implants, (ii) models to study the reaction of implant-associated micro-organisms to antimicrobial agents, and (iii) models to study the reaction of the host tissues to the presence of implants contaminated with micro-organisms. In evaluating the potential usefulness of these models for research in oral implantology, one must consider common features as well as important differences between implanted medical devices and oral implants. Although infections associated with implantable medical devices and oral peri-implant infections share a remarkable number of common features, there are also important differences that need attention when findings from in vitro experiments are extrapolated to clinical relevance.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
The modelling problems associated with the estimation of risks from long-term chemical exposures at low dose levels represent a statistical and mathematical challenge with special relevance to environmental research. Determining an adequate model for estimating the relationship between dose and response is critical to reducing potential bias in the risk estimation process. This paper discusses the various assumptions and models used in carcinogenic risk assessment. The emphasis is on our ability to accurately determine the magnitude of the carcinogenic risk, the shape of the dose-response relationship and the overall variability of the risk estimates.