First observations of separated atmospheric nu_mu and bar{nu-mu} events in the MINOS detector

The complete 5.4 kton MINOS far detector has been taking data since the beginning of August 2003 at a depth of 2070 meters water-equivalent in the Soudan mine, Minnesota. This paper presents the first MINOS observations of nuµ and [overline nu ]µ charged-current atmospheric neutrino interactions based on an exposure of 418 days. The ratio of upward- to downward-going events in the data is compared to the Monte Carlo expectation in the absence of neutrino oscillations, giving Rup/downdata/Rup/downMC=0.62-0.14+0.19(stat.)±0.02(sys.). An extended maximum likelihood analysis of the observed L/E distributions excludes the null hypothesis of no neutrino oscillations at the 98% confidence level. Using the curvature of the observed muons in the 1.3 T MINOS magnetic field nuµ and [overline nu ]µ interactions are separated. The ratio of [overline nu ]µ to nuµ events in the data is compared to the Monte Carlo expectation assuming neutrinos and antineutrinos oscillate in the same manner, giving R[overline nu ][sub mu]/nu[sub mu]data/R[overline nu ][sub mu]/nu[sub mu]MC=0.96-0.27+0.38(stat.)±0.15(sys.), where the errors are the statistical and systematic uncertainties. Although the statistics are limited, this is the first direct observation of atmospheric neutrino interactions separately for nuµ and [overline nu ]µ

Atomic force microscopy analysis of nanoparticles in non-ideal conditions

Nanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions

Molecular targeting of prostate cancer cells by a triple drug combination down-regulates integrin driven adhesion processes, delays cell cycle progression and interferes with the cdk-cyclin axis

Background: Single drug use has not achieved satisfactory results in the treatment of prostate cancer, despite application of increasingly widespread targeted therapeutics. In the present study, the combined impact of the mammalian target of rapamycin (mTOR)-inhibitor RAD001, the dual EGFr and VGEFr tyrosine kinase inhibitor AEE788 and the histone deacetylase (HDAC)-inhibitor valproic acid (VPA) on prostate cancer growth and adhesion in vitro was investigated. Methods: PC-3, DU-145 and LNCaP cells were treated with RAD001, AEE788 or VPA or with a RAD-AEE-VPA combination. Tumor cell growth, cell cycle progression and cell cycle regulating proteins were then investigated by MTT-assay, flow cytometry and western blotting, respectively. Furthermore, tumor cell adhesion to vascular endothelium or to immobilized extracellular matrix proteins as well as migratory properties of the cells was evaluated, and integrin alpha and beta subtypes were analyzed. Finally, effects of drug treatment on cell signaling pathways were determined. Results: All drugs, separately applied, reduced tumor cell adhesion, migration and growth. A much stronger anti-cancer effect was evoked by the triple drug combination. Particularly, cdk1, 2 and 4 and cyclin B were reduced, whereas p27 was elevated. In addition, simultaneous application of RAD001, AEE788 and VPA altered the membranous, cytoplasmic and gene expression pattern of various integrin alpha and beta subtypes, reduced integrin-linked kinase (ILK) and deactivated focal adhesion kinase (FAK). Signaling analysis revealed that EGFr and the downstream target Akt, as well as p70S6k was distinctly modified in the presence of the drug combination. Conclusions: Simultaneous targeting of several key proteins in prostate cancer cells provides an advantage over targeting a single pathway. Since strong anti-tumor properties became evident with respect to cell growth and adhesion dynamics, the triple drug combination might provide progress in the treatment of advanced prostate cancer

Network Models of TEM β-Lactamase Mutations Coevolving under Antibiotic Selection Show Modular Structure and Anticipate Evolutionary Trajectories

Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design