Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Oxidation of Pt(111) under near-ambient conditions

The oxidation of Pt(111) at near-ambient O2 pressures has been followed in situ using x-ray photoelectron spectroscopy (XPS) and ex situ using x-ray absorption spectroscopy (XAS). Polarization-dependent XAS signatures at the O K edge reveal significant temperature- and pressure-dependent changes of the Pt-O interaction. Oxide growth commences via a PtO-like surface oxide that coexists with chemisorbed oxygen, while an ultrathin α-PtO2 trilayer is identified as the precursor to bulk oxidation. These results have important implications for understanding the chemical state of Pt in catalysis.     

Plasma damping effects on the radiative energy loss of relativistic particles

The energy loss of a relativistic charge undergoing multiple scatterings while traversing an infinite, polarizable and absorptive plasma is investigated. Polarization and absorption mechanisms in the medium are phenomenologically modeled by a complex index of refraction. Apart from the known Ter-Mikaelian effect related to the dielectric polarization of matter, we find an additional, substantial reduction of the energy loss due to the damping of radiation. The observed effect is more prominent for larger damping and/or larger energy of the charge. A conceivable analog of this phenomenon in QCD could influence the study of jet quenching phenomena in ultrarelativistic heavy-ion collisions at RHIC and LHC.     

Modelling of Surfactant Concentration in a Coupled Bulk Surface Problem

Surface active agents (surfactants) are on high interest for physical and chemical industries. Their presence lowers the surface tension of fluids. This is used to achieve special flow properties. These mechanisms are not only influenced by the overall amount of surfactant. They highly depend on the local surfactant distribution. The surfactant concentration is modeled by two convection diffusion equations, one in the bulk and the other on its surface. Adsorption- and desorption processes are included and lead to a (non linear) coupling of both equations. We present a numerical scheme for solving the coupled problem. Additionally, first analytical results will be given. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Quasi-particle description of strongly interacting matter: Towards a foundation

We confront our quasi-particle model for the equation of state of strongly interacting matter with recent first-principle QCD calculations. In particular, we test its applicability at finite baryon densities by comparing with Taylor expansion coefficients of the pressure for two quark flavours. We outline a chain of approximations starting from the Φ-functional approach to QCD which motivates the quasi-particle picture.     

MODELING THE DISTRIBUTION OF FISH SCHOOLS IN THE BERING SEA: MORPHOLOGICAL SCHOOL IDENTIFICATION

In this paper we demonstrate how fish schools can be automatically identified from acoustic backscatter data using a simple morphological and threshold algorithm. The algorithm includes: 1) seabed identification and removal; 2) bubble layer removal; 3) choosing a threshold for fish backscatter and setting all pixels below it to 0; and 4) identifying the school boundaries and smoothing the images by using morphological operations. We compared the school sizes and boundaries found by using our algorithms with those found by experts to evaluate our choice of threshold, structuring element and morphological operations. Results showed two classes of images. The algorithm performed well for images having distinct, well defined fish schools. Images having more amorphous aggregations showed significant disparity between the algorithm and experts' choice. Differences appear to involve separating higher intensity aggregations from surrounding lower intensity aggregations. In these cases, the algorithm provides a more consistent and arguably more accurate estimate than the experts. The shoal image data is translated, via a connected-surface-identifier algorithm into a table of information about shoal size, shape and location that allows further analysis of the shoal information with a much smaller data set than the original acoustic images. Such analysis can lead to observing patterns in fish shoal distribution and its relationship to environmental factors and to point-process tests for randomness at different spatial scales which, in turn, allows stochastic modeling of the schooling process. These topics will be presented in subsequent papers.