Imaging spectroscopy and combinatorial mutagenesis of light harvesting II antenna from Rhodobacter capsulatus

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 1994.Includes bibliographical references (leaves 102-112).by Ellen R. Goldman.Ph.D

Use of neural networks to model molecular structure and function

This thesis is a study of some applications of neural networks - a recent computer algorithm - to modelling the structure and function of biologically important molecules. In Chapter 1, an introduction to neural networks is given. An overview of quantitative structure activity relationships (QSARs) is presented. The applications of neural networks to QSAR and to the prediction of structural and functional features of protein and nucleic acid sequences are reviewed. The neural network algorithms used are discussed in Chapter 2. In Chapter 3, a two-layer feed-forward neural network has been trained to recognise an ATP/GTP-binding local sequence motif. A comparably sophisticated statistical method was developed, which performed marginally better than the neural network. In a second study, described in Chapters 4 and 5, one of the largest data sets available for developing a quantitative structure activity relationship - the inhibition of dihydrofolate reductase by 2,4-diamino-6,6-dimethyl-5-phenyldihydrotriazine derivatives has been used to benchmark several computational methods. A hidden-layer neural network, a decision tree and inductive logic programming have been compared with the more established methods of linear regression and nearest neighbour. The data were represented in two ways: by the traditional Hansch parameters and by a new set of descriptors designed to allow the formulation of rules relating the activity of the inhibitors to their chemical structure. The performance of neural networks has been assessed rigourously in two distinct areas of biomolecular modelling; sequence analysis and drug design. The conclusions of these studies are presented in Chapter 6

Studium membránových transportních proteinů z rodiny Nramp/MntH a jejich funkce pomocí biofyzikálních metod

Syntetické peptidy odpovídající svojí sekvencí transmembránovým segmentům TMS1, TMS3 a TMS6 sekundárně aktivního transportního proteinu MntH z bakterie Escherichia coli byly použity jako model pro studium struktury, interakce s modelovými membránami, vzájemné interakce TM segmentů a jejich funkce. Sekundární struktura byla pomocí spektroskopie cirkulárního dichroismu určována v různých prostředích. Studované peptidy interagovaly s lipidovou membránou a získávaly v tomto prostředí helikální konformaci. Elektrofyziologické experimenty dokázaly, že samostatné TMS jsou schopny za určitých podmínek tvořit iontové kanály v modelových biologických membránách. Elektrofyziologické vlastnosti těchto slabě kationtově selektivních kanálů jsou silně závislé na pH okolního prostředí. Mangan, jako fyziologický substrát MntH, zvýšil vodivost kanálů tvořených TMS1 a TMS6 a ovlivnil přechod mezi zavřeným a otevřeným stavem kanálu. Byl pozorován vliv manganu na konformaci všech studovaných peptidů. V případě TMS3 byla přítomnost Mn2+ pro tvorbu iontových kanálů dokonce nezbytná. Bylo dokázáno, že kanál tvořený funkčně důležitým TMS může do určité míry zachovat funkční vlastnosti celého proteinu. Tyto výsledky mohou přispět k porozumění vztahu mezi strukturou a funkcí na molekulární úrovni. I přesto však zůstává nejasné, do...Three synthetic peptides corresponding to transmembrane segments TMS1, TMS3 and TMS6 of secondary-active transporter MntH from Escherichia coli were used as a suitable alternative model enabling to study TMS structure, TMS interaction with membranes, TMS mutual interaction and also function of MntH. The secondary structure of the peptides was estimated in different environments using circular dichroism spectroscopy. These peptides interacted with and adopted helical conformation in lipid membranes. Electrophysiological experiments demonstrated that individual TMS were able under certain conditions to form ion channels in model biological membranes. Electrophysiological properties of these weakly cation-selective ion channels were strongly dependent on surrounding pH. Manganese ion, as a physiological substrate of MntH, enhanced the conductivity of TMS1 and TMS6 channels, influenced the transition between closed and open states and affected the conformation of all studied peptides. For TMS3 Mn2+ was crucial for formation of ion channels. It was shown that a single functionally important TMS can retain some of the functional properties of the full-length protein. These findings can contribute to understanding of structure-function relationship at the molecular level. However, it remains unclear to what extent...Institute of Physics of Charles UniversityFyzikální ústav UKFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Research and development in optical biosensors for determination of toxic environmental pollutants.

The detection of pollutants (such as toxins, heavy metal ions, and pesticides) in water and food plays an important role in human health and safety regulations. Different optical biosensing techniques enabling the monitoring of these compounds were chosen for this study. Low molecular weight (LMW) environmental toxins, such as simazine, atrazine, nonylphenol and T-2 mycotoxin were registered with the methods of surface plasmon resonance (SPR) and the recently developed total internal reflection ellipsometry (TIRE). The immune assay approach was exploited for in situ registration of the above toxins with specific antibodies immobilized onto a gold surface via a polyelectrolyte layer using electrostatic self-assembly (ESA) technique. TIRE showed a higher sensitivity than the SPR technique. The obtained responses of the TIRE method were higher than estimated for the immune binding of single molecules of nonylphenol or T-2 mycotoxin. The mechanism of the binding of large aggregates of these toxins to respective antibodies was suggested as a possible reason for this. The formation of large molecular aggregates of toxin molecules on the surface was later proven by the AFM study.The prototype of the portable sensor array device for water pollution monitoring was based on a SiO[2]/Si[3]n[4] planar waveguide with a sensing window coated with ESA film containing pH sensitive organic chromophore molecules and different enzymes (namely, urease, acetyl- and butyryl-cholinesterase) adsorbed on a disposable nylon membrane. The sensor was capable of registration of enzyme reactions as well as their inhibition by traces of some typical water pollutants, such as heavy metal ions Cd[2+], Pb[2+], and Ni[2+], and pesticides imidacloprid and DVDP over a wide range of concentrations (from 1000 ppb down to 0.1 ppb). A portable prototype sensor array device comprises a fan-beam laser diode, a semi-cylindrical lens, a planar waveguide with a three-channel cell attached, and a CCD array photodetector. Dedicated software was developed for CCD image processing and further data analysis with an artificial neural network.The large internal surface area within a small volume, efficient room-temperature visible photoluminescence and biocompatibility of porous silicon (PS) has stimulated recent interest in its applications for sensor development. The method of spectroscopic ellipsometry was applied to study in situ the adsorption of bovine serum albumin (BSA) into PS. The porosity and amount of adsorbed BSA were determined by fitting the ellipsometric data to the Bruggeman effective medium approximation model. The presence of intermediate adsorbed layers of polyelectrolytes was found to increase protein adsorption

Redes neurais artificiais e modelos de regressão para estimação de variáveis dendrométricas em Pinus taeda L : análise comparativa

Orientador : Prof. Dr. Carlos Roberto SanquettaCoorientadores : Prof. Dr. Renato Vinicius Oliveira Castro, Prof. Dr. Julio Eduardo ArceTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa: Curitiba, 19/12/2016Inclui referências: f. 142-153Resumo: Estudos com Redes Neurais Artificiais (RNA) para o gênero Pinus são incipientes no Brasil. O objetivo deste trabalho foi testar as RNA para estimação da altura, volume e afilamento para povoamentos de Pinus taeda L., bem como avaliar sua aplicabilidade e compará-la com os métodos tradicionais de regressão. Para a estimação de altura por regressão e RNA foram utilizados dados de 304 parcelas permanentes, totalizando 2.454 remedições de parcela em plantios de Pinus taeda L. entre 3 e 21 anos, pertencentes à empresa Klabin S.A. No ajuste dos modelos hipsométricos os dados foram estratificados em ano de plantio, totalizando 11 estratos. Para cada estrato foram ajustados 4 modelos hipsométricos, e esses avaliados de acordo com o coeficiente de correlação ajustado (R² ajust), erro padrão da estimativa (Syx%), gráficos de dispersão de resíduos, além de estatísticas complementares como viés (V), média das diferenças absolutas (MD) e desvio padrão das diferenças (DPD). Os melhores modelos aplicados nos dados de validação foram comparados às RNA, que por sua vez foram treinadas no software Statistica 7.0, em que as 20 melhores redes foram aplicadas aos dados sem estratificação. A RNA com melhor desempenho para estimar a altura total foi comparada com o modelo de regressão por estrato. Para avaliar a precisão dos modelos hipsométricos e RNA para a redução da medição das alturas, os dados de ajuste foram reduzidos em 30% e 60%, e ajustados e treinados respectivamente. Para a estimação de volume total com casca e diâmetros e volumes ao longo do fuste e ainda volume total com casca por meio de funções de afilamento, foram utilizadas 212 cubagens de árvores com diâmetro a 1,30 (dap) entre 5 e 45 cm e aplicados o método da RNA e regressão. Na metodologia de ajuste por regressão os dados foram divididos em 4 estratos e para cada estrato ajustado 4 modelos de volume e 4 modelos de afilamento. O treinamento das RNA e seleção dos modelos de regressão foram realizados com a mesma metodologia da relação hipsométrica. As melhores RNA e modelos de regressão aplicados aos dados de validação foram comparados para verificar as melhores estimativas. Na estimativa de altura, modelos de regressão apresentaram melhor desempenho que as RNA para a base de dados total e na redução de até 60%. Na estimativa de volume total com casca os modelos estratificados por regressão apresentaram resultados mais satisfatórios, enquanto na estimativa do diâmetro e volume ao longo do fuste as RNA foram mais precisas. Conclui-se que, para as estimativas de altura e volume total de Pinus taeda, o uso de modelos de regressão é mais adequado especialmente em função da maior facilidade de aplicação. Já para as estimativas de diâmetros e volumes ao longo do fuste, o uso das RNA é mais adequado em razão de sua maior precisão e maior complexidade de ajuste dos modelos de afilamento. Palavras-chave: desempenho, interações não lineares, modelos de regressão.Abstract: Studies with Artificial Neural Networks (ANN) for the genus Pinus are incipient in Brazil. The main purpose of this work was to test ANN for height, volume and taper estimation for stands of Pinus taeda L., as well as to evaluate its applicability and to compare it with traditional regression methods. For the height estimation by regression and ANN, data from 304 permanent plots were used, totaling 2,454 remeasurements in Pinus taeda L plantations between 3 and 21 years, belonging to the company Klabin S.A. For the hypsometric models the data were stratified in year of planting, totaling 11 strata. For each stratum, 4 hypsometric models were fitted, and these were evaluated according to the adjusted correlation coefficient (R² adjust), standard error of estimative (Syx%), residual scatter plots, as well as complementary statistics, such as bias (V), absolute differences (MD) and standard deviation of differences (DPD). The best models applied in the validation data were compared to ANN trained in the Statistica 7.0 software, in which the 20 best networks were applied to the data without stratification. The best ANN performing to estimate total height was compared with the stratum regression model. To evaluate the accuracy of the hypsometric and ANN models for the height measurements reduction, the data were reduced by 30% and 60%, and adjusted and trained, respectively. For the estimation of the total volume with bark and diameters and volumes along the stem and also total volume with bark by means of tapering functions, 212 trees were scaled with diameter at 1.30 (dbh) between 5 and 45 cm and ANN and regression method was applied. In the regression adjustment method the data were divided into 4 strata and for each stratum adjusted 4 volume models and 4 taper models. The ANN training and regression models selection were performed using the same methodology as the hypsometric relation. The best ANN and regression models applied to the validation data were compared to verify the best estimates. In height estimation, regression models presented better performance than ANN for the total database and data reduction of up to 60%. For the estimation of total volume with bark, the models stratified by regression presented more satisfactory results, whereas for the estimation of diameter and volume along the stem the ANN was more accurate. It was concluded that, for the estimates of height and total volume of Pinus taeda, the use of regression models is more appropriate especially due to the greater ease of application. For the estimation of diameters and volumes along the stem, the use of ANN is more adequate because of the better precision and greater complexity of adjustment of the models of tapering. Keywords: Performance, nonlinear interactions, regression models

Spacelab Science Results Study

Beginning with OSTA-1 in November 1981 and ending with Neurolab in March 1998, a total of 36 Shuttle missions carried various Spacelab components such as the Spacelab module, pallet, instrument pointing system, or mission peculiar experiment support structure. The experiments carried out during these flights included astrophysics, solar physics, plasma physics, atmospheric science, Earth observations, and a wide range of microgravity experiments in life sciences, biotechnology, materials science, and fluid physics which includes combustion and critical point phenomena. In all, some 764 experiments were conducted by investigators from the U.S., Europe, and Japan. The purpose of this Spacelab Science Results Study is to document the contributions made in each of the major research areas by giving a brief synopsis of the more significant experiments and an extensive list of the publications that were produced. We have also endeavored to show how these results impacted the existing body of knowledge, where they have spawned new fields, and if appropriate, where the knowledge they produced has been applied

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app