Lobe growth in foliose lichen thalli

The margins of foliose lichen thalli comprise individual lobes which grow radially and divide. This results in a complex marginal structure in which lobes differing in morphology, state of division, and growth pattern are crowded together. Various aspects of the biology of these lobes are reviewed including their carbohydrate supply, morphology, pattern of division and branching, the effect of lobe overcrowding and interactions between neighbouring lobes. As the thalus grows, lobes become increasingly crowded together and this restricts further lateral growth. Restriction of lobe width may be reponsible for the changes in radial growth rate (RGR) with size observed in foliose species. Various aspects of lobe biology may be responsible for lobe growth variation including the physical independence of lobes from their neighbours, the genetic origin of the lobes, and the pattern of lobe branching. Overall symmetry of a thallus is maintained by a fluctuating pattern of growth of individual lobes in successive months together with competition for space at the margin

Thermalized polarization dynamics of a discrete optical-waveguide system with four-wave mixing

Statistical mechanics of two coupled vector fields is studied in the tight-binding model that describes propagation of polarized light in discrete waveguides in the presence of the four-wave mixing. The energy and power conservation laws enable the formulation of the equilibrium properties of the polarization state in terms of the Gibbs measure with positive temperature. The transition line T=∞ is established beyond which the discrete vector solitons are created. Also in the limit of the large nonlinearity an analytical expression for the distribution of Stokes parameters is obtained, which is found to be dependent only on the statistical properties of the initial polarization state and not on the strength of nonlinearity. The evolution of the system to the final equilibrium state is shown to pass through the intermediate stage when the energy exchange between the waveguides is still negligible. The distribution of the Stokes parameters in this regime has a complex multimodal structure strongly dependent on the nonlinear coupling coefficients and the initial conditions.

Designing intrinsically photostable low band gap polymers:a smart tool combining EPR spectroscopy and DFT calculations

A rapid and efficient method to identify the weak points of the complex chemical structure of low band gap (LBG) polymers, designed for efficient solar cells, when submitted to light exposure is reported. This tool combines Electron Paramagnetic Resonance (EPR) using the 'spin trapping method' coupled with density functional theory modelling (DFT). First, the nature of the short life-time radicals formed during the early-stages of photo-degradation processes are determined by a spin-trapping technique. Two kinds of short life-time radical (R and R′O) are formed after 'short-duration' illumination in an inert atmosphere and in ambient air, respectively. Second, simulation allows the identification of the chemical structures of these radicals revealing the most probable photochemical process, namely homolytical scission between the Si atom of the conjugated skeleton and its pendent side-chains. Finally, DFT calculations confirm the homolytical cleavage observed by EPR, as well as the presence of a group that is highly susceptible to photooxidative attack. Therefore, the synergetic coupling of a spin trapping method with DFT calculations is shown to be a rapid and efficient method for providing unprecedented information on photochemical mechanisms. This approach will allow the design of LBG polymers without the need to trial the material within actual solar cell devices, an often long and costly screening procedure.

Crystal structure and magnetic behaviour of a five-coordinate iron(III) complex of pyridoxal-4-methylthiosemicarbazone

The crystal structure and magnetic properties of a penta-coordinate iron(III) complex of pyridoxal-4-methylthiosemicarbazone, [Fe(Hmthpy)Cl](CHCHSO), are reported. The synthesised ligand and the metal complex were characterised by spectroscopic methods (H NMR, IR, and mass spectroscopy), elemental analysis, and single crystal X-ray diffraction. The complex crystallises as dark brown microcrystals. The crystal data determined at 100(1) K revealed a triclinic system, space group P over(1, ¯) (Z = 2). The ONSCl geometry around the iron(III) atom is intermediate between trigonal bipyramidal and square pyramidal (t = 0.40). The temperature dependence of the magnetic susceptibility (5-300 K) is consistent with a high spin Fe(III) ion (S = 5/2) exhibiting zero-field splitting. Interpretation of these data yielded: D = 0.34(1) cm and g = 2.078(3). © 2007 Elsevier B.V. All rights reserved.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.