Desenvolupament computacional de la semblança molecular quàntica

La present Tesi Doctoral, titulada desenvolupament computacional de la semblança molecular quàntica, tracta, fonamentalment, els aspectes de càlcul de mesures de semblança basades en la comparació de funcions de densitat electrònica.El primer capítol, Semblança quàntica, és introductori. S'hi descriuen les funcions de densitat de probabilitat electrònica i llur significança en el marc de la mecànica quàntica. Se n'expliciten els aspectes essencials i les condicions matemàtiques a satisfer, cara a una millor comprensió dels models de densitat electrònica que es proposen. Hom presenta les densitats electròniques, mencionant els teoremes de Hohenberg i Kohn i esquematitzant la teoria de Bader, com magnituds fonamentals en la descripció de les molècules i en la comprensió de llurs propietats.En el capítol Models de densitats electròniques moleculars es presenten procediments computacionals originals per l'ajust de funcions densitat a models expandits en termes de gaussianes 1s centrades en els nuclis. Les restriccions físico-matemàtiques associades a les distribucions de probabilitat s'introdueixen de manera rigorosa, en el procediment anomenat Atomic Shell Approximation (ASA). Aquest procediment, implementat en el programa ASAC, parteix d'un espai funcional quasi complert, d'on se seleccionen variacionalment les funcions o capes de l'expansió, d'acord als requisits de no negativitat. La qualitat d'aquestes densitats i de les mesures de semblança derivades es verifica abastament. Aquest model ASA s'estén a representacions dinàmiques, físicament més acurades, en quant que afectades per les vibracions nuclears, cara a una exploració de l'efecte de l'esmorteïment dels pics nuclears en les mesures de semblança molecular. La comparació de les densitats dinàmiques respecte les estàtiques evidencia un reordenament en les densitats dinàmiques, d'acord al que constituiria una manifestació del Principi quàntic de Le Chatelier. El procediment ASA, explícitament consistent amb les condicions de N-representabilitat, s'aplica també a la determinació directe de densitats electròniques hidrogenoides, en un context de teoria del funcional de la densitat.El capítol Maximització global de la funció de semblança presenta algorismes originals per la determinació de la màxima sobreposició de les densitats electròniques moleculars. Les mesures de semblança molecular quàntica s'identifiquen amb el màxim solapament, de manera es mesuri la distància entre les molècules, independentment dels sistemes de referència on es defineixen les densitats electròniques. Partint de la solució global en el límit de densitats infinitament compactades en els nuclis, es proposen tres nivells de aproximació per l'exploració sistemàtica, no estocàstica, de la funció de semblança, possibilitant la identificació eficient del màxim global, així com també dels diferents màxims locals. Es proposa també una parametrització original de les integrals de recobriment a través d'ajustos a funcions lorentzianes, en quant que tècnica d'acceleració computacional. En la pràctica de les relacions estructura-activitat, aquests avenços possibiliten la implementació eficient de mesures de semblança quantitatives, i, paral·lelament, proporcionen una metodologia totalment automàtica d'alineació molecular. El capítol Semblances d'àtoms en molècules descriu un algorisme de comparació dels àtoms de Bader, o regions tridimensionals delimitades per superfícies de flux zero de la funció de densitat electrònica. El caràcter quantitatiu d'aquestes semblances possibilita la mesura rigorosa de la noció química de transferibilitat d'àtoms i grups funcionals. Les superfícies de flux zero i els algorismes d'integració usats han estat publicats recentment i constitueixen l'aproximació més acurada pel càlcul de les propietats atòmiques. Finalment, en el capítol Semblances en estructures cristal·lines hom proposa una definició original de semblança, específica per la comparació dels conceptes de suavitat o softness en la distribució de fonons associats a l'estructura cristal·lina. Aquests conceptes apareixen en estudis de superconductivitat a causa de la influència de les interaccions electró-fonó en les temperatures de transició a l'estat superconductor. En aplicar-se aquesta metodologia a l'anàlisi de sals de BEDT-TTF, s'evidencien correlacions estructurals entre sals superconductores i no superconductores, en consonància amb les hipòtesis apuntades a la literatura sobre la rellevància de determinades interaccions.Conclouen aquesta tesi un apèndix que conté el programa ASAC, implementació de l'algorisme ASA, i un capítol final amb referències bibliogràfiques.

An improved algorithm for generating global window brightness temperatures from multiple satellite infrared imagery

An improved algorithm for the generation of gridded window brightness temperatures is presented. The primary data source is the International Satellite Cloud Climatology Project, level B3 data, covering the period from July 1983 to the present. The algorithm rakes window brightness, temperatures from multiple satellites, both geostationary and polar orbiting, which have already been navigated and normalized radiometrically to the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer, and generates 3-hourly global images on a 0.5 degrees by 0.5 degrees latitude-longitude grid. The gridding uses a hierarchical scheme based on spherical kernel estimators. As part of the gridding procedure, the geostationary data are corrected for limb effects using a simple empirical correction to the radiances, from which the corrected temperatures are computed. This is in addition to the application of satellite zenith angle weighting to downweight limb pixels in preference to nearer-nadir pixels. The polar orbiter data are windowed on the target time with temporal weighting to account for the noncontemporaneous nature of the data. Large regions of missing data are interpolated from adjacent processed images using a form of motion compensated interpolation based on the estimation of motion vectors using an hierarchical block matching scheme. Examples are shown of the various stages in the process. Also shown are examples of the usefulness of this type of data in GCM validation.

Algorithm for generating defective graphene sheets

An algorithm is presented for the generation of molecular models of defective graphene fragments, containing a majority of 6-membered rings with a small number of 5- and 7-membered rings as defects. The structures are generated from an initial random array of points in 2D space, which are then subject to Delaunay triangulation. The dual of the triangulation forms a Voronoi tessellation of polygons with a range of ring sizes. An iterative cycle of refinement, involving deletion and addition of points followed by further triangulation, is performed until the user-defined criteria for the number of defects are met. The array of points and connectivities are then converted to a molecular structure and subject to geometry optimization using a standard molecular modeling package to generate final atomic coordinates. On the basis of molecular mechanics with minimization, this automated method can generate structures, which conform to user-supplied criteria and avoid the potential bias associated with the manual building of structures. One application of the algorithm is the generation of structures for the evaluation of the reactivity of different defect sites. Ab initio electronic structure calculations on a representative structure indicate preferential fluorination close to 5-ring defects.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

Molecular tools to investigate Rhizoctonia solani distribution in soil

Real-time PCR protocols were developed to detect and discriminate 11 anastomosis groups (AGs) of Rhizoctonia solani using ribosomal internal transcribed spacer (ITS) regions (AG-1-IA, AG-1-IC, AG-2-1, AG-2-2, AG-4HGI+II, AG-4HGIII, AG-8) or beta-tubulin (AG-3, AG-4HGII, AG-5 and AG-9) sequences. All real-time assays were target group specific, except AG-2-2, which showed a weak cross-reaction with AG-2tabac. In addition, methods were developed for the high throughput extraction of DNA from soil and compost samples. The DNA extraction method was used with the AG-2-1 assay and shown to be quantitative with a detection threshold of 10-7 g of R. solani per g of soil. A similar DNA extraction efficiency was observed for samples from three contrasting soil types. The developed methods were then used to investigate the spatial distribution of R. solani AG-2-1 in field soils. Soil from shallow depths of a field planted with Brassica oleracea tested positive for R. solani AG-2-1 more frequently than soil collected from greater depths. Quantification of R. solani inoculum in field samples proved challenging due to low levels of inoculum in naturally occurring soils. The potential uses of real-time PCR and DNA extraction protocols to investigate the epidemiology of R. solani are discussed.

Synthesis and evaluation of a solid supported molecular tweezer type receptor for cholesterol

A new macroporous stationary phase bearing 'tweezer' receptors that exhibit specificity for cholesterol has been constructed from rigid multifunctional vinylic monomers derived from 3,5-dibromobenzoic acid, propargyl alcohol and cholesterol. The synthesis of the novel tweezer monomer that contains two cholesterol receptor arms using palladium mediated Sonogashira methodologies and carbonate couplings is reported. The subsequent co-polymerisation of this tweezer monomer with a range of cross-linking agents via a 'pseudo' molecular imprinting approach afforded a diverse set of macroporous materials. The selectivity and efficacy of these materials for cholesterol binding was assessed using a chromatographic screening process. The optimum macroporous stationary phase material composition was subsequently used to construct monolithic solid phase extraction columns for use in the selective extraction of cholesterol from multi-component mixtures of structurally related steroids.

Purification and molecular cloning of antimicrobial peptides from Scots pine seedlings

A novel protocol for rapid and efficient purification of antimicrobial peptides from plant seedlings has been developed. Two peptides with antimicrobial activity, designated p1 and p2, were purified nearly to homogeneity from Scots pine seedlings by a combination of sulfuric acid extraction, ammonium sulfate precipitation, heat-inactivation and ion-exchange chromatography on phosphocellulose. Purified proteins had molecular masses of 11 kDa (p1) and 5.8 kDa (p2) and were identified by mass spectrometry as defensin and lipid-transfer protein, respectively. We demonstrated their growth inhibitory effects against a group of phytopathogenic fungi. Furthermore, we report for the first time molecular cloning and characterization of defensin I cDNA from Scots pine. A cDNA expression library from 7 days Scots pine seedlings was generated and used to isolate a cDNA clone corresponding to Scots pine defensin, termed PsDef1. The full-length coding sequence of PsDef1 is 252 bp in length and has an open reading frame capable to encode a protein of 83 amino residues. The deduced sequence has the typical features of plant defensins, including an endoplasmic reticulum signal sequence of 33 aa, followed by a characteristic defensin domain of 50 amino acids representing its active form. The calculated molecular weight of the mature form of PsDef1 is 5601.6 Da, which correlates well with the results of SDS-PAGE analysis. Finally, the antimicrobial properties of PsDef1 against a panel of fungi and bacteria define it as a member of the morphogenic group of plant defensins. (C) 2009 Elsevier Inc. All rights reserved.

Solvent extraction of Am(III) and Eu(III) from nitrate solution using synergistic mixtures of n-tridentate heterocycles and chlorinated cobalt dicarbollide

The separation by solvent extraction of Am-241(III) from Eu-152(III), in 1 M NaNO3 weakly acidic (pH 4) aqueous solutions, into dilute (ca. 10(-2) M) solutions of triazinylbipyridine derivatives (diethylhemi-BTP or di(benzyloxyphenyl) hemi-BTP) and chlorinated cobalt dicarbollide (COSAN) in 1-octanol or nitrobenzene has been studied. The N-tridentate heterocyclic ligands, which are selective for Am(III) over Eu(III), secured efficient separation of the two metal ions, while COSAN, strongly hydrophobic and fully dissociated in polar diluents, enhanced the extraction of the metal ions by ion-pair formation. Molecular interactions between the two co-extractants, observed at higher concentrations, led to the precipitation of their 1: 1 molecular adduct. In spite of that, efficient separations of Am and Eu ions were attained, with high separation factors, SFAm/Eu of 40 and even 60, provided the concentration of hemi-BTP was significantly greater than that of COSAN. Excess COSAN concentrations caused an antagonistic effect, decreasing both the distribution ratio of the metal ions and their separation factor.

Prospects for bio-molecular control using femtosecond transients parametrised in the wavelet domain [poster]

We discuss the parametrisation of am-plitude and phase genes corre-sponding to space encoded femto-second transients in the wavelet domain. Differential evolution is used to improve the speed of con-vergence of the genetic algorithm. We discuss prospects of bio-molecular control using such methodology.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.