Improving Hyperspectral Pixel Classification With Unsupervised Training Data Selection

An unsupervised method for selecting training data is suggested here. The method is tested by applying it to hyperspectral land-use classification. The data set is reduced using an unsupervised band selection method and then clustered with a nonparametric cluster technique. The cluster technique provides centers of the clusters, and those are the samples selected to compose the training set. Both the band selection and the clustering are unsupervised techniques. Afterward, an expert labels those samples, and the rest of unlabeled data can be classified. The inclusion of the selection step, although unsupervised, allows to select automatically the most suitable pixels to build the classifier. This reduces the expert effort because less pixels need to be labeled. However, the classification results are significantly improved in comparison with the results obtained by a random selection of training samples, in particular for very small training sets

Hormony tarczycy i otyłość: znana, lecz słabo rozpoznana relacja

Hormony tarczycy (thyroid hormones, TH) są zaangażowane w wiele różnych procesów biologicznych, wliczając rozwój układu nerwowego, regulację metabolizmu pośredniego oraz zużycie energii. Aktywnie uczestniczą w podstawowym zużyciu energii i termogenezie adaptacyjnej i z tego względu mogą mieć wpływ na masę ciała w przebiegu chorób tarczycy. Otyłość to niezakaźna, przewlekła, zapalna choroba metaboliczna, która implikuje dodatni bilans energetyczny. Tkanka tłuszczowa produkuje szereg hormonów i adipocytokin, takich jak leptyna, które mogą wpływać na stan tarczycy na różnych poziomach. Istnieją dowody na to, że dysfunkcja tarczycy może predysponować do otyłości i odwrotnie, istnieją dowody sugerujące, że otyłość powoduje zmiany dotyczące tarczycy. Celem tej pracy było opisanie związku między układem tarczycy a otyłością. Ponadto w pracy zaprezentowano hipotetyczny model podkreślający znaczenie obwodowej dejodynacji hormonów tarczycy i jego rolę w ustanowieniu dodatniego bilansu energetycznego. Podsumowując, możemy stwierdzić, że relacja między układem tarczycy a otyłością i nadwagą jest złożona i obejmuje wiele poziomów interakcji. Ponadto, poddając ocenie otyłego pacjenta, powinno się rozważyć ocenę funkcji tarczycy, aby uzyskać lepsze i spersonalizowane efekty leczenia

Neuroadaptive changes and behavioral effects after a sensitization regime of MDPV

3,4-methylenedioxypyrovalerone (MDPV) is a synthetic cathinone with cocaine-like properties. In a previous work, we exposed adolescent mice to MDPV, finding sensitization to cocaine effects, and a higher vulnerability to cocaine abuse in adulthood. Here we sought to determine if such MDPV schedule induces additional behavioral-neuronal changes that could explain such results. After MDPV treatment (1.5 mg·kg-1, twice daily, 7 days), mice were behaviorally tested. Also, we investigated protein changes in various brain regions MDPV induced aggressiveness and anxiety, but also contributed to a faster habituation to the open field. This feature co-occurred with an induction of ΔFosB in the orbitofrontal cortex that was higher than its expression in the ventral striatum. Early after treatment, D2R:D1R ratio pointed to a preponderance of D1R but, upon withdrawal, the ratio recovered. Increased expression of Arc, CDK5 and TH, and decrease in DAT protein levels persisted longer after withdrawal, pointing to a neuroplastic lasting effect similar to that involved in cocaine addiction. The implication of the hyperdopaminergic condition in the MDPV-induced aggressiveness cannot be ruled out. We also found an initial oxidative effect of MDPV, without glial activation. Moreover, although initially the dopaminergic signal induced by MDPV resulted in increased ΔFosB, we did not observe any change in NFκB or GluA2 expression. Finally, the changes observed after MDPV treatment could not be explained according to the autoregulatory loop between ΔFosB and the epigenetic repressor G9a described for cocaine. This provides new knowledge about the neuroadaptive changes involved in the vulnerability to psychostimulant addiction

Automated segmentation based upon remitted scatter spectra from pathologically distinct tumor regions

Multi-spectral scatter visualization of tissue ultra-structure in situ can provide a unique tool for guiding surgical resection, but since changes are subtle and the data is multi-parametric, an automated methodology was sought to interpret these data, in order to classify their tissue sub-type. Tissue types observed across AsPC-1 pancreatic tumor samples were pathologically classified under three major groups (epithelium, fibrosis and necrosis) and the variations in scattering parameters, i.e. scattering power, scattering amplitude and average scattered intensity, across these groups were analyzed. The proposed scheme uses statistical pre-processing of the scattering parameter images to create additional data features followed by a k-nearest neighbors (kNN) based algorithm for tissue type classification. The classification accuracy inside some predefined regions of interest was determined and the mean region values of scattering parameters turned out to be stronger data sets for classification, rather than the individual pixel values. This presumably indicates that pixel-to-pixel variations in the remitted spectra need to be minimized for reliable classification approaches. Results show a strong correlation between the automated and expert-based classification within the predefined regions of interest

Automated interpretation of scatter signatures aimed at tissue morphology identification

An automated algorithm and methodology is presented to pathologically classify the scattering changes encountered in the raster scanning of normal and tumor pancreatic tissues using microsampling reflectance spectroscopy. A quasiconfocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal vs. tumor tissue was readily performed using an Artificial Neural Network (ANN) classifier algorithm. A similar approach has worked also for regions of tumor morphology when statistical pre-processing of the scattering parameters was included to create additional data features. This automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging do not reach enough contrast to assist the surgeon

Automated ensemble segmentation of epithelial proliferation, necrosis, and fibrosis using scatter tumor imaging

Conventional imaging systems used today in surgical settings rely on contrast enhancement based on color and intensity and they are not sensitive to morphology changes at the microscopic level. Elastic light scattering spectroscopy has been shown to distinguish ultra-structural changes in tissue. Therefore, it could provide this intrinsic contrast being enormously useful in guiding complex surgical interventions. Scatter parameters associated with epithelial proliferation, necrosis and fibrosis in pancreatic tumors were previously estimated in a quantitative manner. Subtle variations were encountered across the distinct diagnostic categories. This work proposes an automated methodology to correlate these variations with their corresponding tumor morphologies. A new approach based on the aggregation of the predictions of K-nearest neighbors (kNN) algorithm and Artificial Neural Networks (ANNs) has been developed. The major benefit obtained from the combination of the distinct classifiers is a significant increase in the number of pixel localizations whose corresponding tissue type is reliably assured. Pseudo-color diagnosis images are provided showing a strong correlation with sample segmentations performed by a veterinary pathologist

Antecedents and outcomes of consumer environmentally friendly attitudes and behaviour

With the intensification of problems relating to the environment, a growing number of consumers are becoming more ecologically conscious in their preferences and purchases of goods. This paper presents the results of a study conducted among 500 Cypriot consumers, focusing on the factors that shape consumer environmental attitudes and behaviour, as well as on the resulting outcomes. The findings confirmed that both the inward and outward environmental attitudes of a consumer are positively influenced by his/her degree of collectivism, long-term orientation, political involvement, deontology, and law obedience, but have no connection with liberalism. The adoption of an inward environmental attitude was also found to be conducive to green purchasing behaviour that ultimately leads to high product satisfaction. On the other hand, an outward environmental attitude facilitates the adoption of a general environmental behaviour, which is responsible for greater satisfaction with life. The findings of the study have important implications for shaping effective company offerings to consumers in target markets, as well as formulating appropriate policies at the governmental level to enhance environmental sensitivity among citizens