Review

Mrs Jolanta Wadowska-Król, MD, was born in Katowice. She graduated from the Faculty of Medicine of the Medical University of Silesia in Zabrze and in 1968 completed basic specialist training in paediatric medicine. During this period, she started working in a district clinic in Szopienice, and then she worked in Dąbrówka Mała. She served children and youth with her help until 2011. This is how every review usually begins, with the author then focusing on the scientific achievements of the Honorary Doctor. Nevertheless, I will go off the beaten track with the review. I will discuss an exceptional person and a fundamental problem that remains relevant and sorrowful despite the passage of years (Fragment tekstu)

Recenzja

Pani Doktor Jolanta Wadowska-Król urodziła się w Katowicach. Ukończyła studia na Wydziale Lekarskim Śląskiej Akademii Medycznej w Zabrzu, a w 1968 roku zdobyła specjalizację z zakresu pediatrii. W tym okresie rozpoczęła pracę w poradni rejonowej w Szopienicach, a następnie pracowała w Dąbrówce Małej. Służyła swą pomocą dzieciom i młodzieży do 2011 roku. Tak zazwyczaj rozpoczyna się każda recenzja, której autor w dalszej jej części skupia się na naukowych osiągnięciach Doktoranta. Niemniej jednak moja recenzja łamie utarty kanon, gdyż mówię w niej o wyjątkowej osobie i niezwykle ważnym problemie, który mimo upływu lat jest wciąż aktualny i bolesny. Zważywszy na rangę zasług Pani Doktor Jolanty Wadowskiej-Król, które materializują się przede wszystkim w wymiarze moralnym, ludzkim i społecznym, postanowiłem osadzić moją recenzję w historii naszego śląskiego regionu, nakreślić w niej bardzo szeroki kontekst problemu, a także wpleść narrację o pięknej sylwetce osoby, której determinacja i poświęcenie uratowały wiele istnień, w szczególności dzieci (Fragment tekstu)

Near-infrared hyperspectral imaging for polymer particle size estimation

This study examines the potential of near-infrared hyperspectral imaging for assessing the size of polymer particles in model fractions based on the scattering phenomena. Different fractions of ground polymers, either polymethyl methacrylate or polypropylene, were characterized by near-infrared spectra collected between 900 and 1700 nm. The possibility to estimate the size of polymer particles using hyperspectral images was confronted with a basic single spot near-infrared measurement. Hyperspectral imaging, in addition to the standard spectral data dimension, provides information about the spatial distribution of sample components and reveals changes in physical properties. Therefore, one can gain a better insight into the scattering phenomena and study the physical inhomogeneity of a sample in terms of particle size distribution. The partial least-squares models constructed to estimate particle size of polymers that were characterized by hyperspectral images (a pixel-based approach) outperforms models built for mean spectra regardless of the considered powdered polymer

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal’s baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed

Studying the stability of Solvent Red 19 and 23 as excise duty components under the influence of controlled factors

In this study, we examine the chemical stability of two disazo dyes, namely Solvent Red 19 and 23 (SR 19 and SR 23), under simulated conditions. Both dyes are considered to be chemically stable under normal exploitation conditions and therefore, are used extensively as excise duty components that enable a rapid visual verification of the tax levels that were imposed on fuel products as well as identifying fuel usage. However, the results from this study confirmed that the colour of the samples that had been fortified with either SR 19 or SR 23 fades under the influence of external conditions such as UV-A irradiation and temperature over time. The UV-A irradiation was the dominant factor that was responsible for the colour of the samples to fade in two designed experiments that were carried out independently for two model systems. The analysis of the UV/Vis and fluorescence spectra as well as the interpretation of the changes that were observed in the chromatographic profiles provided substantial evidence that the colour fading was caused by the photodegradation of the disazo dyes, which also occurs in non-polar media including fuel products. SR 19 is more stable than SR 23

A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantificatio

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints

In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets

Discrimination of biofilm samples using pattern recognition techniques

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination–DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms

On the orthogonal distance to class subspaces for high-dimensional data classification

The orthogonal distance from an instance to the subspace of a class is a key metric for pattern classification by the class subspace-based methods. There is a close relationship between the orthogonal distance and the residual standard deviation of a test instance from the class subspace. In this paper, we shall show that an established and widely-used relationship, between the residual standard deviation and the sum of squares of the residual PC scores, is not precise, and thus can lead to incorrect results, for the inference of high-dimensional data which nowadays are common in practice

Evaluation of the effect of chance correlations on variable selection using Partial Least Squares -Discriminant Analysis

Variable subset selection is often mandatory in high throughput metabolomics and proteomics. However, depending on the variable to sample ratio there is a significant susceptibility of variable selection towards chance correlations. The evaluation of the predictive capabilities of PLSDA models estimated by cross-validation after feature selection provides overly optimistic results if the selection is performed on the entire set and no external validation set is available. In this work, a simulation of the statistical null hypothesis is proposed to test whether the discrimination capability of a PLSDA model after variable selection estimated by cross-validation is statistically higher than that attributed to the presence of chance correlations in the original data set. Statistical significance of PLSDA CV-figures of merit obtained after variable selection is expressed by means of p-values calculated by using a permutation test that included the variable selection step. The reliability of the approach is evaluated using two variable selection methods on experimental and simulated data sets with and without induced class differences. The proposed approach can be considered as a useful tool when no external validation set is available and provides a straightforward way to evaluate differences between variable selection methods.JE and JK acknowledge the "Sara Borrell" Grants (CD11/00154 and CD12/00667) from the Instituto Carlos III (Ministry of Economy and Competitiveness). DPG acknowledge the "V Segles" Grant provided by the University of Valencia to carry out this study. MV acknowledges the FISPI11/0313 Grant from the Instituto Carlos III (Ministry of Economy and Competitiveness). AF acknowledges the DPI2011-28112-C04-02 Grant from Spanish Ministry of Science and Innovation (MICINN). GQ acknowledges the financial support from the Spanish Ministry of Economy and Competitivity (SAF2012-39948).Kuligowski, J.; Pérez Guaita, D.; Escobar, J.; Guardia, MDL.; Vento, M.; Ferrer Riquelme, AJ.; Quintás, G. (2013). Evaluation of the effect of chance correlations on variable selection using Partial Least Squares -Discriminant Analysis. Talanta. 116:835-840. https://doi.org/10.1016/j.talanta.2013.07.048S83584011