No abstract

Modified nucleosides, regarded as indicators for the whole-body turnover of RNAs, are excreted in abnormal amounts in the urine of patients with malignancies. To test their usefulness as tumour markers and to compare them with the conventional tumour markers, fractionated urine samples were analysed using chromatography. The excretion patterns of nucleosides of 68 cancer patients with malignant and benign tumours and 41 healthy controls have been studied. Significant elevations in the total sum and the concentrations of at least three (or four) of indicator nucleosides cytidine, pseudouridine, 2-pyridone-5-carboxamide-N1-ribofuranoside, N2,N2-dimethylguanine, 1-methylguanosine, 2-methylguanosine and 1-methyladenosine indicate a tumour with a sensitivity of 54% (77%) and a specificity of 86% (98%). Using an artificial neural network analysis, a sensitivity of 97% and a specificity of 85% were achieved in differentiating between tumour and control volunteers. The comparison with carcinoembryonic antigen, cancer antigen 15-3 und tissue polypeptide antigen indicates that urinary nucleosides may be useful tumour markers. This study suggests that the simultaneous determination of modified nucleosides and creatinine in urine samples of patients with cancer leads to an advantage to current methods and is a useful method to detect cancer early and to control the success of therapy.

The displacive phase transition in SrTiO3 was investigated by means of x-ray diffraction. We used 4.5 keV photons thus probing only a very thin region near the surface. In the low temperature phase the lattice parameters evolve substantially different than in bulk material. We also investigated the phase transition under the influence of an epitaxial coating with YBaCu2O7 and found the nature of the phase transition changed. The near-surface region behaves like an epitaxial thin SrTiO3 film.

Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image feature extraction using orthogonal total variation component analysis enables a strong reduction in dimensionality and memory size of each input dataset, while maintaining the majority of its spatial and spectral information. This is in particular advantageous for sensors with very high spatial and/or spectral resolution, which are otherwise difficult to jointly process due to their large data memory requirements during classification. The extracted features are not only bound to absorption features but recognize specific and relevant spatial or spectral patterns. We exemplify the workflow with data acquired with five commercially available hyperspectral sensors and a pair of RGB cameras. The robust and efficient spectral-spatial procedure is evaluated on a representative set of geological samples. We validate the process with independent and detailed mineralogical and spectral data. The suggested workflow provides a versatile solution for the integration of multi-source hyperspectral data in a diversity of geological applications. In this study, we show a straight-forward integration of visible/near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) data for sensors with highly different spatial and spectral resolution that greatly improves drillcore mapping.