Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

In Situ Near-Infrared (NIR) Versus High-Throughput Mid- Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical Production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

In situ NIR spectroscopy monitoring of plasmid production processes effect of producing strain, medium composition and the cultivation strategy

BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coliDH5 cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655endArecApgi and MG1655endArecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSIONNIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used.

Monitoring the ex-vivo expansion of human mesenchymal stem/stromal cells in xeno-free microcarrier-based reactor systems by MIR spectroscopy

Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

Predicting Xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments’ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”

Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin production

Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.

Dual-phase inkjet printed electrochromic layers based on PTA and WOX/TiO2 nanoparticles for electrochromic applications

Thesis submitted in Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa for the degree of Master in Materials Engineering

Evaluation of the double diffusion, enzyme immunoassay and immunoblotting techniques, for the diagnosis of human hydatid disease in tropical areas

Hydatid disease in tropical areas poses a serious diagnostic problem due to the high frequence of cross-reactivity with other endemic helminthic infections. The enzyme-linked-immunosorbent assay (ELISA) and the double diffusion arc 5 showed respectively a sensitivity of 73% and 57% and a specificity of 84-95% and 100%. However, the specificity of ELISA was greatly increased by using ovine serum and phosphorylcholine in the diluent buffer. The hydatic antigen obtained from ovine cyst fluid showed three main protein bands of 64,58 and 30 KDa using SDS PAGE and immunoblotting. Sera from patients with onchocerciasis, cysticercosis, toxocariasis and Strongyloides infection cross-reacted with the 64 and 58 KDa bands by immunoblotting. However, none of the analyzed sera recognized the 30 KDa band, that seems to be specific in this assay. The immunoblotting showed a sensitivity of 80% and a specificity of 100% when used to recognize the 30 KDa band.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

Modern techniques for constraint solving the CASPER experience

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Validação da antropometria e ultrassonografia do braço, para medição dos respectivos compartimentos, em lactentes nascidos pré-termo : contributo para a avaliação corporal do recem-nascido e do lactente

RESUMO: Contexto: Indicadores fidedignos da composição corporal são importantes na orientação das estratégias nutricionais de recém-nascidos e pequenos lactentes submetidos a cuidados intensivos. O braço é uma região acessível para avaliar a composição corporal regional, pela medida dos seus compartimentos. A antropometria e a ultrassonografia (US) são métodos não invasivos, relativamente económicos, que podem ser usados à cabeceira do paciente na medição desses compartimentos, embora esses métodos não tenham ainda sido validados neste subgrupo etário. A ressonância magnética (RM) pode ser usada como método de referência na validação da medição dos compartimentos do braço. Objectivo: Validar em lactentes pré-termo, as medidas do braço por antropometria e por US. Métodos: Foi estudada uma coorte de recém-nascidos admitidos consecutivamente na unidade de cuidados intensivos neonatais, com  33 semanas de idade de gestação e peso adequado para a mesma, sem anomalias congénitas major e não submetidas a diuréticos ou oxigenoterapia no momento da avaliação. Nas vésperas da alta, foram efectuadas medições do braço, com ocultação, pelos métodos antropométrico, ultrassonográfico e RM. As medidas antropométricas directas foram: peso (P), comprimento (C), perímetro cefálico (PC), perímetro braquial (PB) e prega cutânea tricipital (PT). As área braquial total, área muscular (AM) e área adiposa foram calculadas pelos métodos de Jeliffee & Jeliffee e de Rolland-Cachera. Utilizando uma sonda PSH-7DLT de 7 Hz no ecógrafo Toshiba SSH 140A foram medidos os perímetros braquial e muscular e calculadas automaticamente as áreas braquial e muscular, sendo a área adiposa obtida por subtracção. Como método de referência foi utilizada a RM – Philips Gyroscan ACS-NT, Power-Track 1000 ®, 1.5 Tesla com uma antena de quadratura do joelho. Na análise estatística foram utilizados os métodos paramétricos e não paramétricos, conforme adequado. Resultados: Foram incluídas 30 crianças, nascidas com ( ±DP) 30.7 ±1.9 semanas de gestação, pesando 1380 ±325g, as quais foram avaliadas às 35.4 ±1.1 semanas de idade corrigida, quando pesavam 1786 ±93g. Nenhuma das medidas antropométricas, individualmente, constitui um indicador aceitável (r2 <0.5) das medições por RM. A melhor e mais simples equação alternativa encontrada é a que estima a AM (r2 = 0.56), derivada dos resultados da análise de regressão múltipla: AMRM = (P x 0.17) + (PB x 5.2) – (C x 6) – 150, sendo o P expresso em g, o C e o PB em cm. Nenhuma das medidas ultrassonográficas constitui um indicador aceitável (r2 <0.4) das medições por RM. Conclusões: A antropometria e as medidas ultrassonográficas do braço não são indicadores fidedignos da composição corporal regional em lactentes pré-termo, adequados para a idade de gestação.----------ABSTRACT: Background: Accurate predictors for body composition are valuable tools guiding nutritional strategies in infants needing intensive care. The upper-arm is a part of the body that is easily accessible and convenient for assessing the regional body composition, throughout the assessment of their compartments. Anthropometry and by ultrasonography (US) are noninvasive and relatively nonexpensive methods for bedside assessment of the upper-arm compartments. However, these methods have not yet been validated in infants. Magnetic resonance imaging (MRI) may be used as gold standard to validate the measurements of the upper-arm compartments. Objective: To validate the upper-arm measurements by anthropometry and by US in preterm infants. Methods: A cohort of neonates consecutively admitted at the neonatal intensive care unit, appropriate for gestational age, with  33 weeks, without major congenital abnormalities and not subjected to diuretics or oxygen therapy, was assessed. Before the discharge, the upper-arm was blindly measured by anthropometry, US and MRI. The direct anthropometric parameters measured were: weight (W), length (L), head circumference (HC), mid-arm circumference (MAC), and tricipital skinfold thickness. The arm area (AA), arm muscle area (AMA) and arm fat area were calculated applying the methods proposed by Jeliffee & Jeliffee and by Rolland-Cachera. Using the sonolayer Toshiba SSH 140A and the probe PSH-7DLT 7Hz, the arm and muscle perimeters were measured by US, the arm and muscle areas included were automatically calculated, and the fat area was calculated by subtraction. The MR images were acquired on a 1.5-T Philips Gyroscan ACS-NT, Power-Track 1000 scanner, and a knee coil was chosen for the upper-arm measurements. For statistical analysis parametric and nonparametric methods were used as appropriate. Results: Thirty infants born with ( ±SD) 30.7 ±1.9 weeks of gestational age and weighing 1380 ±325g were included in the study; they were assessed at 35.4 ±1.1 weeks of corrected age, weighing 1786 ±93g. None of the anthropometric measurements are individually acceptable (r2 <0.5) for prediction of the measurements obtained by MRI. The best and simple alternative equation found is the equation for prediction of the AMA (r2 = 0.56), derived from the results of multiple regression analysis: AMARM = (W x 0.17) + (MAC x 5.2) – (L x 6) – 150, being the W expressed in g, and L and MAC in cm. None of the ultrasonographic measurements are acceptable (r2 <0.5) predictors for the measurements obtained by MRI. Conclusions: The measurements of the upper-arm by anthropometry and by US are not accurate predictors for the regional body composition in preterm appropriate for gestational age infants.

Detection of mycoplasmas in urethral swabs from HIV-1 infected patients and control individuals using culture techniques and polymerase chain reaction

The objective of the present study was to determine the prevalence of certain mycoplasma species, i.e., Mycoplasma hominis, Ureaplasma urealyticum and Mycoplasma penetrans, in urethral swabs from HIV-1 infected patients compared to swabs from a control group. Mycoplasmas were detected by routine culture techniques and by the Polymerase Chain Reaction (PCR) technique, using 16SrRNA generic primers of conserved region and Mycoplasma penetrans specific primers. The positivity rates obtained with the two methods were comparable. Nevertheless, PCR was more sensitive, while the culture techniques allowed the quantification of the isolates. The results showed no significant difference (p < 0.05) in positivity rates between the methods used for mycoplasma detection.

PALEOPARASITOLOGY: PERSPECTIVES WITH NEW TECHNIQUES

Paleoparasitology is the study of parasites found in archaeological material. The development of this field of research began with histological identification of helminth eggs in mummy tissues, analysis of coprolites, and recently through molecular biology. An approach to the history of paleoparasitology is reviewed in this paper, with special reference to the studies of ancient DNA identified in archaeological material.