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”

A competing values framework approach on growth and performance in micro firms

This study aims to analyze and compare four micro-firms' organizational culture, evaluated through the Competing Values Framework (Quinn & Rohbaugh, 1983). Data was collected in 2011 and 2013 in firms selling the same type of software and providing the same kind of services, focusing on the years between 2008-2011. Findings point to somewhat different results of micro-firms, when comparing to other samples in the literature. Suggestions for future research are given.

Exposure to polycyclic aromatic hydrocarbons and assessment of potential risks in preschool children

As children represent one of the most vulnerable groups in society, more information concerning their exposure to health hazardous air pollutants in school environments is necessary. Polycyclic aromatic hydrocarbons (PAHs) have been identified as priority air pollutants due to their mutagenic and carcinogenic properties that strongly affect human health. Thus, this work aims to characterize levels of 18 selected PAHs in preschool environment, and to estimate exposure and assess the respective risks for 3–5-year-old children (in comparison with adults). Gaseous PAHs (mean of 44.5 ± 12.3 ng m−3) accounted for 87 % of the total concentration (ΣPAHs) with 3–ringed compounds being the most abundant (66 % of gaseous ΣPAHs). PAHs with 5 rings were the most abundant ones in the particulate phase (PM; mean of 6.89 ± 2.85 ng m−3) being predominantly found in PM1 (78 % particulate ΣPAHs). Overall child exposures to PAHs were not significantly different between older children (4–5 years old) and younger ones (3 years old). Total carcinogenic risks due to particulate-bound PAHs indoors were higher than outdoor ones. The estimated cancer risks of both preschool children and the staff were lower than the United States Environmental Protection Agency (USEPA) threshold of 10−6 but slightly higher than WHO-based guideline.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Eruca sativa: Benefits as antioxidants source versus risks of already banned pesticides

Eruca sativa (rocket salad) has been intensely consumed all over the world, insomuch as, this work was undertaken to evaluate the antioxidant status and the environmental contamination (positive and negative nutritional contribution) of leaves and stems from this vegetable. Antioxidant capacity of rocket salad was assessed by mean of optical methods, such as the total phenolic content (TPC), reducing power assay and DPPH radical scavenging activity. The extent of the environmental contamination was reached through the quantification of thirteen organochlorine pesticides (OCP) by using gas chromatography coupled with electron-capture detector (GC-ECD) and compound confirmations employing gas chromatography tandem mass-spectrometry (GC-MS/MS). The OCP residues were extracted by using Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) methodology.The extent of the environmental contamination was reached through the quantification of thirteen OCP by using gas chromatography coupled with electron-capture detector (GC-ECD) and compound confirmations employing GC-MS/MS. The OCP residues were extracted by using Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) methodology. This demonstrated that leaves presented more antioxidant activity than stems, emphasizing that leaves contained six times more polyphenolic compounds than stems. In what concerns the OCP occurrence, the average recoveries obtained at the three levels tested (40, 60 and 80 µg kg−1) ranged from 55% to 149% with a relative standard deviation of 11%, (except hexachrorobenzene). Three vegetables samples were collected from supermarkets and analysed following this study. According to data, only one sample achieved 16.21 of β-hexachlorocyclohexane, confirmed by GC-MS/MS. About OCP quantification, the data indicated that only one sample achieved 16.21 µg kg−1 of β-hexachlorocyclohexane, confirmed by GC-MS/MS, being the QuEChERS a good choice for the of OCPs extraction. Furthermore, the leaves consumption guaranty higher levels of antioxidants than stems.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.