Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization /

Solid state nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for studying structural and dynamical properties of disordered and partially ordered materials, such as glasses, polymers, liquid crystals, and biological materials. In particular, twodimensional( 2D) NMR methods such as ^^C-^^C correlation spectroscopy under the magicangle- spinning (MAS) conditions have been used to measure structural constraints on the secondary structure of proteins and polypeptides. Amyloid fibrils implicated in a broad class of diseases such as Alzheimer's are known to contain a particular repeating structural motif, called a /5-sheet. However, the details of such structures are poorly understood, primarily because the structural constraints extracted from the 2D NMR data in the form of the so-called Ramachandran (backbone torsion) angle distributions, g{^,'4)), are strongly model-dependent. Inverse theory methods are used to extract Ramachandran angle distributions from a set of 2D MAS and constant-time double-quantum-filtered dipolar recoupling (CTDQFD) data. This is a vastly underdetermined problem, and the stability of the inverse mapping is problematic. Tikhonov regularization is a well-known method of improving the stability of the inverse; in this work it is extended to use a new regularization functional based on the Laplacian rather than on the norm of the function itself. In this way, one makes use of the inherently two-dimensional nature of the underlying Ramachandran maps. In addition, a modification of the existing numerical procedure is performed, as appropriate for an underdetermined inverse problem. Stability of the algorithm with respect to the signal-to-noise (S/N) ratio is examined using a simulated data set. The results show excellent convergence to the true angle distribution function g{(j),ii) for the S/N ratio above 100.

Solid state NMR chemical shifts as an alternative to diffraction data in the determination of SIC polytypic structures

Silicon carbide, which has many polytypic modifications of a very simple and very symmetric structure, is an excellent model system for exploring, the relationship between chemical shift, long-range dipolar shielding, and crystal structure in network solids. A simple McConnell equation treatment of bond anisotropy effects in a poly type predicts chemical shifts for silicon and carbon sites which agree well with the experiment, provided that contributions from bonds up to 100 A are included in the calculation. The calculated chemical shifts depend on three factors: the layer stacking sequence, electrical centre of gravity, and the spacings between silicon and carbon layers. The assignment of peaks to lattice sites is proved possible for three polytypes (6H, 15R, and 3C). The fact that the calculated chemical shifts are very sensitive to layer spacings provides us a potential way to detennine and refine a crystal structure. In this work, the layer spacings of 6H SiC have been calculated and are within X-ray standard deviations. Under this premise, the layer spacings of 15R have been detennined. 29Si and 13C single crystal nmr studies of 6H SiC polytype indicate that all silicons and carbons are magnetically anisotropic. The relationship between a magnetic shielding tensor component and layer spacings has been derived. The comparisons between experimental and semi-empirical chemical shielding tensor components indicate that the paramagnetic shielding of silicon should be included in the single crystal chemical shift calculation.

The crystal and molecular structure of bis (pyridoxamine) copper (II) dinitrate monohydrate

The crystal structure of Cu(PM)2(N03hoH20 (where PM is pyridoxamine, CSHI2N202) has been determined from three dimensional x-ray diffraction data. The crystals are triclinic, space group pI, a = 14.248 (2), b = 8.568 (1), c = 9.319 (1) 1, a = 94.08 (1), e = 89.73 (1), y~~ 99.18 (1)°, z = 2, jl(MoK) = 10.90 em-I, Po = 1.61 g/cm3 and Pc = 1.61 g/em3• The structure a was solved by Patterson techniques from data collected on a Picker 4-circle diffractometer to 26max = 45°. All atoms, including hydrogens, have been located. Anisotropic thermal parameters have been refined for all nonhydrogen atoms. For the 2390 independent reflections with F ? 3cr(F) , R = 0.0408. The results presented here provide the first detailed structural information of a metal complex with PM itself. The copper atoms are located on centres of symmetry and each is chela ted by two PM zwitterions through the amino groups and phenolate oxygen atoms. The zwitterionic form found in this structure involves the loss of a proton from the phenolate group and protonation of the pyridine ring nitrogen atoms. The two independent Cu(PM)2 moieties are symmetrically bridged by a single oxygen atom from one of the nitrate groups. The second nitrate group is not coordinated to the copper atoms but is central to an extensive hydrogen bonding network involving the water molecule and uncoordinated functional groups of PM.

The effectiveness of explicit strategy instruction on the top level structure of a text

Thesis (M.Ed.)-- Brock University, 1995.

The relationship between stem-form, stand-closure and site -conditions : the influence of environmental conditions on tree allometry and forest structure in west-central Alberta

Changes in the configuration of a tree stern result insignificant differences in its total volume and in the proportion of that volume that is merchantable timber. Tree allometry, as represented by stem-fo~, is the result of the vertical force of gravity and the horizontal force of wind. The effect of wind force is demonstrated in the relationship between stem-form, standclosure and site-conditions. An increase in wind force on the individual tree due to a decrease in stand density should produce a more tapered tree. The density of the stand is determined by the conditions that the trees are growing under. The ability of the tree to respond to increased wind force may also be a function of these conditions . This stem-form/stand-closure/site-conditions relationship was examined using a pre-existing database from westcentral Alberta. This database consisted of environmental, vegetation, soils and timber data covering a wide range of sites. There were 653 sample trees with 82 variables that formed the basis of the analysis. There were eight tree species consisting of Pinus contorta, Picea mariana, Picea engelmannii x glauca, Abies lasiocarpa, Larix laricina, Populus tremuloides, Betula papyrifera and Populus balsamifera plus a comprehensive all-species data set. As the actual conformation of the stern is very individual, stem-fo~was represented by the diameter at breast height to total height r~tio. The four stand-closure variables, crown closure, total basal area, total volume and total number of stems were reduced to total basal area and total number of stems utilizing a bivariate correlation matrix by species. Site-conditions were subdivided into macro, meso and micro variables and reduced in number 3 using cross-tabulations, bivariate correlation and principal components analysis as screening tools. The stem-fo~/stand-closure relationship was examined using bivariate correlation coefficients for stem-fo~ with total number of stems and stem-fo~ with total basal area. The stem-fo~/site-conditions and the stand-closure/site- conditions relationships were examined using multiple correlation coefficients. The stem-form/stand-closure/site-conditions relationship was examined using multiple correlation coefficients in separate analyses for both total number of stems and total basal area. An increase in stand-closure produced a decrease in stem-form for both total number of stems and total basal area for most species. There was a significant relationship between stem-form and site-conditions and between stand-closure and site-conditions for both total number of stems and total basal area for most species. There was a significant relationship between the stemform and site-conditions, including the stand-closure, for most species; total number of stems was involved independently of the site-conditions in the prediction of stem-form and total basal area was not. Larix laricina and Betula papyrifera were the exceptions to the trends observed with most species. The influence of both stand-closure (total number of stems in particular) and site-conditions (elevation in particular) suggest that forest management practices should include these- ecological parameters in determining appropriate restocking levels.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.