Characterization of redox centers in dimethyl sulfide dehydrogenase from Rhodovulum sulfidophilum

Dimethyl sulfide dehydrogenase from the purple phototrophic bacterium Rhodovulum sulfidophilum catalyzes the oxidation of dimethyl sulfide to dimethyl sulfoxide. Recent DNA sequence analysis of the ddh operon, encoding dimethyl sulfide dehydrogenase (ddhABC), and biochemical analysis (1) have revealed that it is a member of the DMSO reductase family of molybdenum enzymes and is closely related to respiratory nitrate reductase (NarGHI). Variable temperature X-band EPR spectra (120122 K) of purified heterotrimeric dimethyl sulfide dehydrogenase showed resonances arising from multiple redox centers, Mo(V), [3Fe-4S](+), [4Fe-4S](+), and a b-type heme. A pH-dependent EPR study of the Mo(V) center in (H2O)-H-1 and (H2O)-H-2 revealed the presence of three Mo(V) species in equilibrium, Mo(V)-OH2, Mo(v)-anion, and Mo(V)-OH. Above pH 8.2 the dominant species was Mo(V)-OH. The maximum specific activity occurred at pH 9.27. Comparison of the rhombicity and anisotropy parameters for the Mo(V) species in DMS dehydrogenase with other molybdenum enzymes of the DMSO reductase family showed that it was most similar to the low-pH nitrite spectrum of Escherichia coli nitrate reductase (NarGHI), consistent with previous sequence analysis of DdhA and NarG. A sequence comparison of DdhB and NarH has predicted the presence of four [Fe-S] clusters in DdhB. A [3Fe-4S](+) cluster was identified in dimethyl sulfide dehydrogenase whose properties resembled those of center 2 of NarH. A [4Fe-4S](+) cluster was also identified with unusual spin Hamiltonian parameters, suggesting that one of the iron atoms may have a fifth non-sulfur ligand. The g matrix for this cluster is very similar to that found for the minor conformation of center 1 in NarH [Guigliarelli, B., Asso, M., More, C., Augher, V., Blasco, F., Pommier, J., Giodano, G., and Bertrand, P. (1992) Eur. J. Biochem. 307,63-68]. Analysis of a ddhC mutant showed that this gene encodes the b-type cytochrome in dimethyl sulfide dehydrogenase. Magnetic circular dichroism studies revealed that the axial ligands to the iron in this cytochrome are a histidine and methionine, consistent with predictions from protein sequence analysis. Redox potentiometry showed that the b-type cytochrome has a high midpoint redox potential (E-o = +315 mV, pH 8).

A survey of Australian mental health occupational therapists

Mental health reform in the western developed world has resulted in new models of care and changed work practices for all mental health professionals. Occupational therapists, as with other mental health professionals, have been required to assume new roles and responsibilities. Literature from the United Kingdom has reflected concern about this new way of working. The aim of this exploratory study was to examine the current work practices of and issues faced by Australian mental health occupational therapists. One hundred and forty-eight respondents (74%) answered an occupational therapy practice in mental health questionnaire. The results from this survey suggest that there are two quite distinct groups of occupational therapists working in mental health settings in Australia. One group works as rehabilitation therapists in traditional activity-focused work roles. The other group works as case manager therapists and employs a much broader spectrum of clinical and support roles. The issues facing therapists include the development and maintenance of a clearly defined role, generic case management versus discipline-specific roles, recruitment and retention, the need for research and evidence-based practice, professional standing, and education and professional development. The concerns over the role of occupational therapy in mental health were similar to those in previous British studies. The implications of these findings include a need for education and training at the undergraduate and postgraduate levels to equip mental health occupational therapists with both discipline-specific and generic skills.

Application of the maximum entropy method to the (2 + 1)D four-fermion model

We investigate spectral functions extracted using the maximum entropy method from correlators measured in lattice simulations of the (2+1)-dimensional four-fermion model. This model is particularly interesting because it has both a chirally broken phase with a rich spectrum of mesonic bound states and a symmetric phase where there are only resonances. In the broken phase we study the elementary fermion, pion, sigma, and massive pseudoscalar meson; our results confirm the Goldstone nature of the π and permit an estimate of the meson binding energy. We have, however, seen no signal of σ→ππ decay as the chiral limit is approached. In the symmetric phase we observe a resonance of nonzero width in qualitative agreement with analytic expectations; in addition the ultraviolet behavior of the spectral functions is consistent with the large nonperturbative anomalous dimension for fermion composite operators expected in this model.

A new method for analysing the equilibrium and time-dependent behaviour of Markovian models

Many large-scale stochastic systems, such as telecommunications networks, can be modelled using a continuous-time Markov chain. However, it is frequently the case that a satisfactory analysis of their time-dependent, or even equilibrium, behaviour is impossible. In this paper, we propose a new method of analyzing Markovian models, whereby the existing transition structure is replaced by a more amenable one. Using rates of transition given by the equilibrium expected rates of the corresponding transitions of the original chain, we are able to approximate its behaviour. We present two formulations of the idea of expected rates. The first provides a method for analysing time-dependent behaviour, while the second provides a highly accurate means of analysing equilibrium behaviour. We shall illustrate our approach with reference to a variety of models, giving particular attention to queueing and loss networks. (C) 2003 Elsevier Ltd. All rights reserved.

Valuing of firm's prior knowledge: A measure of knowledge distance

Knowledge, especially scientific and technological knowledge, grows according to knowledge trajectories and guideposts that make up the prior knowledge of an organization. We argue that these knowledge structures and their specific components lead to successful innovation. A firm's prior knowledge facilitates the absorption of new knowledge, thereby renewing a firm's systematic search, transfer and acquisition of knowledge and capabilities. In particular, the exponential growth in biotechnology is characterized by the convergence of disparate scientific and technological knowledge resources. This paper examines the shift from protein-based to DNA-based diagnostic technologies as an example, to quantify the value of a firm's prior knowledge using relative values of knowledge distance. The distance between core prior knowledge and the rate of transition from one knowledge system to another has been identified as a proxy for the value a firm's prior knowledge. The overall difficulty of transition from one technology paradigm to another is discussed. We argue this transition is possible when the knowledge distance is minimal and the transition process has a correspondingly high value of absorptive capacities. Our findings show knowledge distance is a determinant of the feasibility, continuity and capture of scientific and technological knowledge. Copyright © 2003 John Wiley & Sons, Ltd.

The morbidity of Guillain-Barré syndrome admitted to the intensive care unit

Patients with severe forms of Guillain-Barré syndrome (GBS) require intensive care. Specific treatment, catheterization, and devices may increase morbidity in the intensive care unit (ICU). To understand the spectrum of morbidity associated with ICU care, the authors studied 114 patients with GBS. Major morbidity occurred in 60% of patients. Complications were uncommon if ICU stay was less than 3 weeks. Respiratory complications such as pneumonia and tracheobronchitis occurred in half of the patients and were linked to mechanical ventilation. Systemic infection occurred in one-fifth of patients and was more frequent with increasing duration of ICU admission. Direct complications of treatment and invasive procedures occurred infrequently. Life-threatening complications such as gastrointestinal bleeding and pulmonary embolism were very uncommon. Pulmonary morbidity predominates in patients with severe GBS admitted to the ICU. Attention to management of mechanical ventilation and weaning is important to minimize this complication of GBS. Other causes of morbidity in a tertiary center ICU are uncommon.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.

Mass spectrometry improvement on an high current ion implanter

The development of accurate mass spectrometry, enabling the identification of all the ions extracted from the ion source in a high current implanter is described. The spectrometry system uses two signals (x-y graphic), one proportional to the magnetic field (x-axes), taken from the high-voltage potential with an optic fiber system, and the other proportional to the beam current intensity (y-axes), taken from a beam-stop. The ion beam mass register in a mass spectrum of all the elements magnetically analyzed with the same radius and defined by a pair of analyzing slits as a function of their beam intensity is presented. The developed system uses a PC to control the displaying of the extracted beam mass spectrum, and also recording of all data acquired for posterior analysis. The operator uses a LabView code that enables the interfacing between an I/O board and the ion implanter. The experimental results from an ion implantation experiment are shown. (C) 2011 Elsevier B.V. All rights reserved.

Topological Complexity and Predictability in the Dynamics of a Tumor Growth Model with Shilnikov's Chaos

Dynamical systems modeling tumor growth have been investigated to determine the dynamics between tumor and healthy cells. Recent theoretical investigations indicate that these interactions may lead to different dynamical outcomes, in particular to homoclinic chaos. In the present study, we analyze both topological and dynamical properties of a recently characterized chaotic attractor governing the dynamics of tumor cells interacting with healthy tissue cells and effector cells of the immune system. By using the theory of symbolic dynamics, we first characterize the topological entropy and the parameter space ordering of kneading sequences from one-dimensional iterated maps identified in the dynamics, focusing on the effects of inactivation interactions between both effector and tumor cells. The previous analyses are complemented with the computation of the spectrum of Lyapunov exponents, the fractal dimension and the predictability of the chaotic attractors. Our results show that the inactivation rate of effector cells by the tumor cells has an important effect on the dynamics of the system. The increase of effector cells inactivation involves an inverse Feigenbaum (i.e. period-halving bifurcation) scenario, which results in the stabilization of the dynamics and in an increase of dynamics predictability. Our analyses also reveal that, at low inactivation rates of effector cells, tumor cells undergo strong, chaotic fluctuations, with the dynamics being highly unpredictable. Our findings are discussed in the context of tumor cells potential viability.

Use of solvent extraction to remediate soils contaminated with hydrocarbons

The main objective of this research is to exploit the possibility of using an ex situ solvent extraction technique for the remediation of soils contaminated with semi-volatile petroleum hydrocarbons. The composition of the organic phase was chosen in order to form a single phase mixture with an aqueous phase and simultaneously not being disturbed (forming stable emulsions) by the soil particles hauling the contaminants. It should also permit a regeneration of the organic solvent phase. As first, we studied the miscibility domain of the chosen ternary systems constituted by ethyl acetate–acetone–water. This system proved to satisfy the previous requirements allowing for the formation of a single liquid phase mixture within a large spectrum of compositions, and also allowing for an intimate contact with the soil. Contaminants in the diesel range within different functional groups were selected: xylene, naphthalene and hexadecane. The analytical control was done by gas chromatography with FID detector. The kinetics of the extractions proved to be fast, leading to equilibrium after 10 min. The effect of the solid–liquid ratio on the extraction efficiency was studied. Lower S/L ratios (1:8, w/v) proved to be more efficient, reaching recoveries in the order of 95%. The option of extraction in multiple contacts did not improve the recovery in relation to a single contact. The solvent can be regenerated by distillation with a loss around 10%. The contaminants are not evaporated and they remain in the non-volatile phase. The global results show that the ex situ solvent extraction is technically a feasible option for the remediation of semi-volatile aromatic, polyaromatic and linear hydrocarbons.

Growth pressure dependence of Cu2ZnSnSe4 properties

In this work, we show a set of growth conditions, for the two step process, with which the growth of CZTSe is successful and reproducible. The properties of the best CTZSe thin films grown by this method were examined by SEM/EDS, XRD, Raman scattering, AFM/EFM, transmittance and reflectance measurements, photoluminescence (PL) measurements and hot point probe. A broad emission band was observed in the photoluminescence spectrum of the CZTSe thin film. The band gap energy was estimated to be around 1.05 eV at room temperature, using the transmittance and reflectance data, and CZTSe samples show p-type conductivity with the hot point probe. The different characterization techniques show that we could grow single phase CZTSe thin films with our optimized process conditions.

Semantics of logic programs with explicit negation

After a historical introduction, the bulk of the thesis concerns the study of a declarative semantics for logic programs. The main original contributions are: ² WFSX (Well–Founded Semantics with eXplicit negation), a new semantics for logic programs with explicit negation (i.e. extended logic programs), which compares favourably in its properties with other extant semantics. ² A generic characterization schema that facilitates comparisons among a diversity of semantics of extended logic programs, including WFSX. ² An autoepistemic and a default logic corresponding to WFSX, which solve existing problems of the classical approaches to autoepistemic and default logics, and clarify the meaning of explicit negation in logic programs. ² A framework for defining a spectrum of semantics of extended logic programs based on the abduction of negative hypotheses. This framework allows for the characterization of different levels of scepticism/credulity, consensuality, and argumentation. One of the semantics of abduction coincides with WFSX. ² O–semantics, a semantics that uniquely adds more CWA hypotheses to WFSX. The techniques used for doing so are applicable as well to the well–founded semantics of normal logic programs. ² By introducing explicit negation into logic programs contradiction may appear. I present two approaches for dealing with contradiction, and show their equivalence. One of the approaches consists in avoiding contradiction, and is based on restrictions in the adoption of abductive hypotheses. The other approach consists in removing contradiction, and is based in a transformation of contradictory programs into noncontradictory ones, guided by the reasons for contradiction.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Fractional order models of leaves

Leaves are mainly responsible for food production in vascular plants. Studying individual leaves can reveal important characteristics of the whole plant, namely its health condition, nutrient status, the presence of viruses and rooting ability. One technique that has been used for this purpose is Electrical Impedance Spectroscopy, which consists of determining the electrical impedance spectrum of the leaf. In this paper we use EIS and apply the tools of Fractional Calculus to model and characterize six species. Two modeling approaches are proposed: firstly, Resistance, Inductance, Capacitance electrical networks are used to approximate the leaves’ impedance spectra; afterwards, fractional-order transfer functions are considered. In both cases the model parameters can be correlated with physical characteristics of the leaves.

Use of medicines recommended for secondary prevention of acute coronary syndrome

ABSTRACT OBJECTIVE : To analyze if the demographic and socioeconomic variables, as well as percutaneous coronary intervention are associated with the use of medicines for secondary prevention of acute coronary syndrome. METHODS : In this cohort study, we included 138 patients with acute coronary syndrome, aged 30 years or more and of both sexes. The data were collected at the time of hospital discharge, and after six and twelve months. The outcome of the study was the simultaneous use of medicines recommended for secondary prevention of acute coronary syndrome: platelet antiaggregant, beta-blockers, statins and angiotensin-converting-enzyme inhibitor or angiotensin receptor blocker. The independent variables were: sex, age, education in years of attending, monthly income in tertiles and percutaneous coronary intervention. We described the prevalence of use of each group of medicines with their 95% confidence intervals, as well as the simultaneous use of the four medicines, in all analyzed periods. In the crude analysis, we verified the outcome with the independent variables for each period through the Chi-square test. The adjusted analysis was carried out using Poisson Regression. RESULTS : More than a third of patients (36.2%; 95%CI 28.2;44.3) had the four medicines prescribed at the same time, at the moment of discharge. We did not observe any differences in the prevalence of use in comparison with the two follow-up periods. The most prescribed class of medicines during discharge was platelet antiaggregant (91.3%). In the crude analysis, the demographic and socioeconomic variables were not associated to the outcome in any of the three periods. CONCLUSIONS : The prevalence of simultaneous use of medicines at discharge and in the follow-ups pointed to the under-utilization of this therapy in clinical practice. Intervention strategies are needed to improve the quality of care given to patients that extend beyond the hospital discharge, a critical point of transition in care.