No description available.

Organophosphorus hydrolase (OPH, EC 3.1.8.1) is a homodimeric enzyme that has been observed to unfold via a three-state unfolding pathway (N2* ↔ I2 ↔ 2U) under chemical denaturing conditions. The dimeric intermediate (I2) is catalytically inactive and, although this enzyme has a very large overall conformational stability (~40 kcal/mol), it takes only a small amount of energy (~4 kcal/mol) to unfold this enzyme into its inactive form. So that this enzyme might be engineered as a more effective tool for nerve agent countermeasures and bioremediation purposes, its operational stability (the energy required to unfold the enzyme from its active, dimeric state to its inactive, dimeric state) must be increased. For this purpose, it is necessary to understand how the enzyme unfolds into its inactive, intermediate state. As tryptophan residues are sensitive probes of the microenvironment surrounding the residue, enzyme variants consisting of one tryptophan per subunit were constructed. Unfortunately, these variant enzymes did not fold into active conformations, and so could not be used to develop an accurate unfolding profile for the wild type enzyme. Limited proteolysis of OPH by thermolysin revealed detailed information on the unfolding process of OPH in chemical and thermal denaturing conditions. Mild denaturing conditions induced an initial enhancement of activity with a subsequent loss of catalytic activity upon more aggressive treatment. Under thermal conditions from 35 – 55 °C, the enzyme developed a well populated and active intermediate that displayed maximal activity. Similarly, the enzyme displayed maximal activity when incubated at 1.0 M urea. The regions of the enzyme, which became accessible to proteolysis at 45 °C and 1 M urea, were identical. This suggested that increased flexibility of these regions was coupled with the increase in the enzyme’s catalytic activity. Two regions that were determined by limited proteolysis to be the first to unfold were bridged with a novel disulfide bond. The result was an enzyme with an increased operational stability and resistance to proteolysis. This enzyme retained approximately 70% of its original activity in 8 M urea while no activity remained for the wild type enzyme when incubated in 6.5 M urea.

Every second, an enormous volume of data is being gathered from various sources and stored in huge data banks. Most of the time, monitoring a data source requires several parallel measurements, which form a high-dimensional sample vector. Due to the curse of dimensionality, applying machine learning methods, that is, studying and analyzing high-dimensional data, could be difficult. The essential task of dimensionality reduction is to faithfully represent a given set of high-dimensional data samples with a few variables. The goal of this thesis is to develop and propose new techniques for handling high-dimensional data, in order to address contemporary demand in machine learning applications. Most prominent nonlinear dimensionality reduction methods do not explicitly provide a way to handle out-of-samples. The starting point of this thesis is a nonlinear technique, called Embedding by Affine Transformations (EAT), which reduces the dimensionality of out-of-sample data as well. In this method, a convex optimization is solved for estimating a transformation between the high-dimensional input space and the low-dimensional embedding space. To the best of our knowledge, EAT is the only distance-preserving method for nonlinear dimensionality reduction capable of handling out-of-samples. The second method that we propose is TesseraMap. This method is a scalable extension of EAT. Conceptually, TesseraMap partitions the underlying manifold of data into a set of tesserae and then unfolds it by constructing a tessellation in a low-dimensional subspace of the embedding space. Crucially, the desired tessellation is obtained through solving a small semidefinite program; therefore, this method can efficiently handle tens of thousands of data points in a short time. The final outcome of this thesis is a novel method in dimensionality reduction called Isometric Patch Alignment (IPA). Intuitively speaking, IPA first considers a number of overlapping flat patches, which cover the underlying manifold of the high-dimensional input data. Then, IPA rearranges the patches and stitches the neighbors together on their overlapping parts. We prove that stitching two neighboring patches aligns them together; thereby, IPA unfolds the underlying manifold of data. Although this method and TesseraMap have similar approaches, IPA is more scalable; it embeds one million data points in only a few minutes. More importantly, unlike EAT and TesseraMap, which unfold the underlying manifold by stretching it, IPA constructs the unfolded manifold through patch alignment. We show this novel approach is advantageous in many cases. In addition, compared to the other well-known dimensionality reduction methods, IPA has several important characteristics; for example, it is noise tolerant, it handles non-uniform samples, and it can embed non-convex manifolds properly. In addition to these three dimensionality reduction methods, we propose a method for subspace clustering called Low-dimensional Localized Clustering (LDLC). In subspace clustering, data is partitioned into clusters, such that the points of each cluster lie close to a low-dimensional subspace. The unique property of LDLC is that it produces localized clusters on the underlying manifold of data. By conducting several experiments, we show this property is an asset in many machine learning tasks. This method can also be used for local dimensionality reduction. Moreover, LDLC is a suitable tool for forming the tesserae in TesseraMap, and also for creating the patches in IPA.

No description available.

This thesis covers work on two topics: unfolding polyhedra into the plane and reconstructing polyhedra from partial information. For each topic, we describe previous work in the area and present an array of new research and results. Our work on unfolding is motivated by the problem of characterizing precisely when overlaps will occur when a polyhedron is cut along edges and unfolded. By contrast to previous work, we begin by classifying overlaps according to a notion of locality. This classification enables us to focus upon particular types of overlaps, and use the results to construct examples of polyhedra with interesting unfolding properties. The research on unfolding is split into convex and non-convex cases. In the non-convex case, we construct a polyhedron for which every edge unfolding has an overlap, with fewer faces than all previously known examples. We also construct a non-convex polyhedron for which every edge unfolding has a particularly trivial type of overlap. In the convex case, we construct a series of example polyhedra for which every unfolding of various types has an overlap. These examples disprove some existing conjectures regarding algorithms to unfold convex polyhedra without overlaps. The work on reconstruction is centered around analyzing the computational complexity of a number of reconstruction questions. We consider two classes of reconstruction problems. The first problem is as follows: given a collection of edges in space, determine whether they can be rearranged <em>by translation only</em> to form a polygon or polyhedron. We consider variants of this problem by introducing restrictions like convexity, orthogonality, and non-degeneracy. All of these problems are NP-complete, though some are proved to be only weakly NP-complete. We then consider a second, more classical problem: given a collection of edges in space, determine whether they can be rearranged by <em>translation and/or rotation</em> to form a polygon or polyhedron. This problem is NP-complete for orthogonal polygons, but polynomial algorithms exist for non-orthogonal polygons. For polyhedra, it is shown that if degeneracies are allowed then the problem is NP-hard, but the complexity is still unknown for non-degenerate polyhedra.

This thesis covers work on two topics: unfolding polyhedra into the plane and reconstructing polyhedra from partial information. For each topic, we describe previous work in the area and present an array of new research and results. Our work on unfolding is motivated by the problem of characterizing precisely when overlaps will occur when a polyhedron is cut along edges and unfolded. By contrast to previous work, we begin by classifying overlaps according to a notion of locality. This classification enables us to focus upon particular types of overlaps, and use the results to construct examples of polyhedra with interesting unfolding properties. The research on unfolding is split into convex and non-convex cases. In the non-convex case, we construct a polyhedron for which every edge unfolding has an overlap, with fewer faces than all previously known examples. We also construct a non-convex polyhedron for which every edge unfolding has a particularly trivial type of overlap. In the convex case, we construct a series of example polyhedra for which every unfolding of various types has an overlap. These examples disprove some existing conjectures regarding algorithms to unfold convex polyhedra without overlaps. The work on reconstruction is centered around analyzing the computational complexity of a number of reconstruction questions. We consider two classes of reconstruction problems. The first problem is as follows: given a collection of edges in space, determine whether they can be rearranged <em>by translation only</em> to form a polygon or polyhedron. We consider variants of this problem by introducing restrictions like convexity, orthogonality, and non-degeneracy. All of these problems are NP-complete, though some are proved to be only weakly NP-complete. We then consider a second, more classical problem: given a collection of edges in space, determine whether they can be rearranged by <em>translation and/or rotation</em> to form a polygon or polyhedron. This problem is NP-complete for orthogonal polygons, but polynomial algorithms exist for non-orthogonal polygons. For polyhedra, it is shown that if degeneracies are allowed then the problem is NP-hard, but the complexity is still unknown for non-degenerate polyhedra.

First measurements are made of the underlying event in calorimeter jet events at the LHC, using 37 pb-1 of pp collisions at √s = 7TeV, recorded during 2010 by the ATLAS detector. Results are compared for an assumed di-jet topology based on a single identified jet, and an exclusive di-jet requirement. The number of charged particles in the azimuthal region transverse to the jet axis is recorded, as well as their total and average transverse momentum. The total energy carried by all particles - charged and neutral - is also calculated, using the full calorimeter acceptance |η| < 4:8. Distributions are constructed to show the variation of these quantities versus the transverse momentum of the selected jet, over the range 20 - 800 GeV. Additional jets in the transverse region are shown to dramatically influence the measured activity. Software is developed to perform Bayesian iterative unfolding, testing closure of the process and stability with respect to the number of iterations performed. Pseudo-experiments are used to propagate systematic errors, and the intrinsic error due to unfolding is estimated. Although the correction relies on a prior probablitity distribution, model-dependence is reduced to an uncertainty comparable to or smaller than experimental systematic errors. The software is used to correct underlying event measurements for effects introduced by the ATLAS detector. Unfolded results are compared to predictions from different Monte Carlo event generators used in LHC analyses, showing general agreement in the range |η| < 2:5, but discrepancies in the forward region. Comparison with other ATLAS results shows compatible behaviour in events defined by any high-momentum charged particle, or by leptonic Z-boson decays.

Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.

Yes / It is important for the formulators of biopharmaceuticals to predict the folding–unfolding transition of proteins. This enables them to process proteins under predetermined conditions, without denaturation. Depending on the apparent denaturation temperature (Tm) of lysozyme, we have derived an equation describing its folding–unfolding transition diagram. According to the water content and temperature, this diagram was divided into three different areas, namely, the area of the water-folded lysozyme phase, the area of the water-folded lysozyme phase and the bulk water phase, and the area of the denatured lysozyme phase. The water content controlled the appearance and intensity of the Raman band at ∼1787 cm–1 when lysozyme powders were thermally denatured at temperatures higher than Tm. / MAM gratefully acknowledges CARA (Stephen Wordsworth and Ryan Mundy) and University of Bradford for providing an academic fellowship.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-06-27T12:24:48Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese _Sandro Vagner de Lima.pdf: 6041788 bytes, checksum: 30432ac952cb4559dfe9e27b22cd9bf5 (MD5) / Made available in DSpace on 2016-06-27T12:24:49Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese _Sandro Vagner de Lima.pdf: 6041788 bytes, checksum: 30432ac952cb4559dfe9e27b22cd9bf5 (MD5) Previous issue date: 2015-10-08 / Esta tese de doutorado foi dedicada à investigação do modo como a técnica de espectroscopia de impedância elétrica (EIE) poderia ser usada para acompanhar os processos de mudanças conformacionais de macromoléculas biológicas, como proteínas e DNA. Para isso, usamos como sistemas modelos a proteína albumina do soro bovino (BSA), e a formação do complexo polianilina/DNA (PANI/DNA). Com a caracterização de soluções de DNA e BSA por EIE e sua modelagem elétrica convenientemente descrita pelo circuito de Randles (e sua variante), foram determinados os parâmetros relevantes para descrição dos fenômenos de desnaturação e de agregação da proteína e da precipitação do complexo PANI/DNA. As informações obtidas sobre a solubilidade desses últimos complexos são de grande utilidade para o entendimento dos mecanismos de interação entre cadeias de DNA e de polímeros condutores. Do mesmo ponto de vista da EIE, as sucessivas mudanças da conformação da proteína e os detalhes da cinética de sua agregação na interação com surfactantes foram adequadamente correlacionados com a característica elétrica do circuito de Randles das soluções correspondentes. Finalmente, estudos iniciais foram estendidos para a análise dos processos de fibrilação de proteínas. Para todos os problemas abordados, o uso da resistência de transferência de carga elétrica (RCT) (um parâmetro do circuito de Randles) nos permite sugerir ser a técnica de EIE apropriada para caracterizar as diferentes mudanças conformacionais envolvidas em fenômenos que resultam da interação de biomoléculas com moléculas de prova. Assim, ela se confirma como um método competitivo quando comparado ao uso da fluorescência e da absorção UV-Vis (técnicas rotineiramente adotadas para a análise desses problemas). / This doctoral thesis was devoted to the investigation of the technique of electrical impedance spectroscopy as an alternative method to assess conformational changes of biological macromolecules, such as proteins and DNA. For this, we used protein bovine serum albumin (BSA), and the formation of polyaniline (PANI)/DNA complexes as model systems. With the characterization of DNA and BSA solutions by Electrical Impedance Spectroscopy (EIS) and their electrical modeling conveniently described by the Randles circuit (and its variant), we determined the relevant characteristics of phenomena such as the denaturation and aggregation of proteins (BSA), and polymer/DNA complex formation (PANI/DNA). As a result of this approach we identified the existence of different interaction regimes between the chains of polyaniline and DNA molecules that are dependent on the concentration of PANI/DNA and the existence of equilibrium conditions which separate regions of precipitation/stability the PANI/DNA complex. Also from this point of view, the modes of interaction BSA / surfactants involved in the conformation changes well as typical stages associated with fibrillation kinetics were adequately correlated with the electric characteristic of the Randles circuit. In all studies carry out in this thesis, the analysis of the electric charge transfer resistance behavior (RCT) (a parameter of the Randles circuit) when confronted with the results obtained by standard techniques showed that the EIS presents reliable and some comparative advantages. These results allow us to provide an adequate and competitive alternative to conventional methods such as UV-Visible absorption, fluorescence and the use of probe molecules