Cassava wastewater as a substrate for the simultaneous production of rhamnolipids and polyhydroxyalkanoates by Pseudomonas aeruginosa

Glycerol, cassava wastewater (CW), waste cooking oil and CW with waste frying oils were evaluated as alternative low-cost carbon substrates for the production of rhamnolipids and polyhydroxyalkanoates (PHAs) by various Pseudomonas aeruginosa strains. The polymers and surfactants produced were characterized by gas chromatography-mass spectrophotometry (MS) and by high-performance liquid chromatography-MS, and their composition was found to vary with the carbon source and the strain used in the fermentation. The best overall production of rhamnolipids and PHAs was obtained with CW with frying oil as the carbon source, with PHA production corresponding to 39% of the cell dry weight and rhamnolipid production being 660 mg l(-1). Under these conditions, the surface tension of the culture decreased to 30 mN m(-1), and the critical micelle concentration was 26.5 mg l(-1). It would appear that CW with frying oil has the highest potential as an alternative substrate, and its use may contribute to a reduction in the overall environmental impact generated by discarding such residues.

Simultaneous analysis of five antidepressant drugs using direct injection of biofluids in a capillary restricted-access media-liquid chromatography-tandem mass spectrometry system

Direct analysis, with minimal sample pretreatment, of antidepressant drugs, fluoxetine, imipramine, desipramine, amitriptyline, and nortriptyline in biofluids was developed with a total run time of 8 min. The setup consists of two HPLC pumps, injection valve, capillary RAM-ADS-C18 pre-column and a capillary analytical C 18 column connected by means of a six-port valve in backflush mode. Detection was performed with ESI-MS/MS and only 1 mu m of sample was injected. Validation was adequately carried out using FLU-d(5) as internal standard. Calibration curves were constructed under a linear range of 1-250 ng mL(-1) in plasma, being the limit of quantification (LOQ), determined as 1 ng mL(-1), for all the analytes. With the described approach it was possible to reach a quantified mass sensitivity of 0.3 pg for each analyte (equivalent to 1.1-1.3 fmol), translating to a lower sample consumption (in the order of 103 less sample than using conventional methods). (C) 2008 Elsevier B.V. All rights reserved.

Simultaneous Differential Pulse Voltammetric Determination of Ascorbic Acid and Caffeine in Pharmaceutical Formulations Using a Boron-Doped Diamond Electrode

A cathodically pretreated boron-doped diamond electrode was used for the simultaneous anodic determination of ascorbic acid (AA) and caffeine (CAF) by differential pulse voltammetry Linear calibration curves (r = 0 999) were obtained from 1 9 x 10(-5) to 2 I x 10(-4) mol L(-1) for AA and from 9 7 x 10(-6) to 1 1 x 10-4 mol L(-1) for CAF. with detection limits of 19 wool L(-1) and 7 0 mu nol L(-1). respectively This method was successfully applied for the determination of AA and CAF in pharmaceutical formulations. with results equal to those obtained using a HPLC reference method

Simultaneous voltammetric determination of paracetamol and caffeine in pharmaceutical formulations using a boron-doped diamond electrode

A simple and highly selective electrochemical method was developed for the single or simultaneous determination of paracetamol (N-acetyl-p-aminophenol, acetaminophen) and caffeine (3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione) in aqueous media (acetate buffer, pH 4.5) on a boron-doped diamond (BDD) electrode using square wave voltammetry (SWV) or differential Pulse voltammetry (DPV). Using DPV with the cathodically pre-treated BDD electrode, a separation of about 550 mV between the peak oxidation potentials Of paracetamol and caffeine present in binary mixtures was obtained. The calibration curves for the simultaneous determination of paracetamol and caffeine showed an excellent linear response, ranging from 5.0 x 10(-7) mol L(-1) to 8.3 x 10(-7) mol L(-1) for both compounds. The detection limits for the simultaneous determination of paracetamol and caffeine were 4.9 x 10(-7) mol L-1 and 3.5 x 10(-8) mol L(-1), respectively. The proposed method Was Successfully applied in the simultaneous determination of paracetamol and caffeine in several pharmaceutical formulations (tablets), with results similar to those obtained using a high-performance liquid chromatography method (at 95% confidence level). (C) 2008 Elsevier BY. All rights reserved.

Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

Residual-based Inference for Common Nonlinear Features

This paper investigates common nonlinear features in multivariate nonlinear autore-gressive models via testing the estimated residuals. A Wald-type test is proposed and itis asymptotically Chi-squared distributed. Simulation studies are given to examine thefinite-sample properties of the proposed test.

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Previsão em tempo atual de cheias com uso de sistema especialista difuso

Sistemas de previsão de cheias podem ser adequadamente utilizados quando o alcance é suficiente, em comparação com o tempo necessário para ações preventivas ou corretivas. Além disso, são fundamentalmente importantes a confiabilidade e a precisão das previsões. Previsões de níveis de inundação são sempre aproximações, e intervalos de confiança não são sempre aplicáveis, especialmente com graus de incerteza altos, o que produz intervalos de confiança muito grandes. Estes intervalos são problemáticos, em presença de níveis fluviais muito altos ou muito baixos. Neste estudo, previsões de níveis de cheia são efetuadas, tanto na forma numérica tradicional quanto na forma de categorias, para as quais utiliza-se um sistema especialista baseado em regras e inferências difusas. Metodologias e procedimentos computacionais para aprendizado, simulação e consulta são idealizados, e então desenvolvidos sob forma de um aplicativo (SELF – Sistema Especialista com uso de Lógica “Fuzzy”), com objetivo de pesquisa e operação. As comparações, com base nos aspectos de utilização para a previsão, de sistemas especialistas difusos e modelos empíricos lineares, revelam forte analogia, apesar das diferenças teóricas fundamentais existentes. As metodologias são aplicadas para previsão na bacia do rio Camaquã (15543 km2), para alcances entre 10 e 48 horas. Dificuldades práticas à aplicação são identificadas, resultando em soluções as quais constituem-se em avanços do conhecimento e da técnica. Previsões, tanto na forma numérica quanto categorizada são executadas com sucesso, com uso dos novos recursos. As avaliações e comparações das previsões são feitas utilizandose um novo grupo de estatísticas, derivadas das freqüências simultâneas de ocorrência de valores observados e preditos na mesma categoria, durante a simulação. Os efeitos da variação da densidade da rede são analisados, verificando-se que sistemas de previsão pluvio-hidrométrica em tempo atual são possíveis, mesmo com pequeno número de postos de aquisição de dados de chuva, para previsões sob forma de categorias difusas.

A dynamic model of simultaneous price competition with switching and search costs

Esse artigo apresenta um modelo dinâmico de competição em precos que incorpora tanto custos de procura quanto custos de switching e onde que as decisões do consumidor e das firmas são simultâneas. Dadas as hipóteses feitas n ós veremos que este modelo possui equilí brio. As principais propriedades do equil íbrio deste modelo são: Se os custos de procura forem baixos o suficiente, em equilí brio o consumidor vai procurar todas as firmas no mercado enquanto que o aumento dos custos de procura vai reduzir a propor cão de firmas que o consumidor busca. Um resultado contraintuitivo e que os pre cos esperados pagos pelo consumidor normalmente decresce em nossas computa cões numéricas do equil íbrio quando os custos de procura aumentam. Enquanto que aumentar os custos de switching tamb ém vai produzir o resultado contraituitivo que as firmas unmatched vão diminuir suas ofertas de modo a atrair o consumidor.

Semiparametric estimation and inference using doubly robust moment conditions

We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance functions that are estimated in a first stage, but retain a fully nonparametric model instead. We call these estimators semiparametric doubly robust estimators (SDREs), and show that they possess superior theoretical and practical properties compared to generic semiparametric two-step estimators. In particular, our estimators have substantially smaller first-order bias, allow for a wider range of nonparametric first-stage estimates, rate-optimal choices of smoothing parameters and data-driven estimates thereof, and their stochastic behavior can be well-approximated by classical first-order asymptotics. SDREs exist for a wide range of parameters of interest, particularly in semiparametric missing data and causal inference models. We illustrate our method with a simulation exercise.