Limit on sterile neutrino contribution from the Mainz Neutrino Mass Experiment

The recent analysis of the normalization of reactor antineutrino data, the calibration data of solar neutrino experiments using gallium targets, and the results from the neutrino oscillation experiment MiniBooNE suggest the existence of a fourth light neutrino mass state with a mass of O(eV), which contributes to the electron neutrino with a sizable mixing angle. Since we know from measurements of the width of the Z0 resonance that there are only three active neutrinos, a fourth neutrino should be sterile (i.e., interact only via gravity). The corresponding fourth neutrino mass state should be visible as an additional kink in beta-decay spectra. In this work the phase II data of the Mainz Neutrino Mass Experiment have been analyzed searching for a possible contribution of a fourth light neutrino mass state. No signature of such a fourth mass state has been found and limits on the mass and the mixing of this fourth mass states are derived

Doping high Tc superconductors with oxygen and metallic atoms: A molecular dynamics study

Using classical molecular dynamics based on Lennard-Jones-like potentials, a mechanically stable YBa2Cu3O7 high Tc superconductor structure is generated. This process is controlled via interactive computer graphics. After doping atoms into or removing atoms from the sample using a recently implemented picking mechanism, the lattice oscillation energy is annihilated with a simulated annealing procedure. The remaining minimum ground state energy allows marking of the preferred doping location. Information on the doping mechanism is important because the magnetic and superconducting properties of these compounds depend very strongly on their oxygen conten

Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks

Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain “decide” what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensitizing the brain to perceive the same again (hysteresis), or a repulsive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. This areal and hierarchical segregation may explain how the brain maintains the balance between exploiting redundancies and staying sensitive to new information. We provide a Bayesian model that accounts for the coexistence of hysteresis and adaptation by separating their causes into 2 distinct terms: Hysteresis alters the prior, whereas adaptation changes the sensory evidence (the likelihood function)

Targeting aquatic microcontaminants for monitoring: exposure categorization and application to the Swiss situation

Background, aim, and scope: Aquatic microcontaminants (MCs) comprise diverse chemical classes, such as pesticides, biocides, pharmaceuticals, consumer products, and industrial chemicals. For water pollution control and the evaluation of water protection measures, it is crucial to screen for MCs. However, the selection and prioritization of which MCs to screen for is rather difficult and complex. Existing methods usually are strongly limited because of a lack of screening regulations or unavailability of required data. Method and models: Here, we present a simple exposure-based methodology that provides a systematic overview of a broad range of MCs according to their potential to occur in the water phase of surface waters. The method requires input of publicly available data only. Missing data are estimated with quantitative structure-property relationships. The presented substance categorization methodology is based on the chemicals' distribution behavior between different environmental media, degradation data, and input dynamics. Results: Seven different exposure categories are distinguished based on different compound properties and input dynamics. Ranking the defined exposure categories based on a chemical's potential to occur in the water phase of surface waters, exposure categories I and II contain chemicals with a very high potential, categories III and IV contain chemicals with a high potential, and categories V and VI contain chemicals with a moderate to low potential. Chemicals in category VII are not evaluated because of a lack of data. We illustrate and evaluate the methodology on the example of MCs in Swiss surface waters. Furthermore, a categorized list containing potentially water-relevant chemicals is provided. Discussion: Chemicals of categories I and III continuously enter surface waters and are thus likely to show relatively steady concentrations. Therefore, they are best suited for water monitoring programs requiring a relatively low sampling effort. Chemicals in categories II and IV have complex input dynamics. They are consequently more difficult to monitor. However, they should be considered if an overall picture is needed that includes contaminants from diffuse sources. Conclusions: The presented methodology supports compound selection for (a) water quality guidance, (b) monitoring programs, and (c) further research on the chemical's ecotoxicology. The results from the developed categorization procedure are supported by data on consumption and observed concentrations in Swiss surface waters. The presented methodology is a tool to preselect potential hazardous substances based on exposure-based criteria for policy guidance and monitoring programs and a first important step for a detailed risk assessment for potential microcontaminant

A verified equivalent-circuit model for slotwaveguide modulators

We formulate and experimentally validate an equivalent-circuit model based on distributed elements to describe the electric and electro-optic (EO) properties of travellingwave silicon-organic hybrid (SOH) slot-waveguide modulators. The model allows to reliably predict the small-signal EO frequency response of the modulators exploiting purely electrical measurements of the frequency-dependent RF transmission characteristics. We experimentally verify the validity of our model, and we formulate design guidelines for an optimum trade-off between optical loss due to free-carrier absorption (FCA), electro-optic bandwidth, and {\pi}-voltage of SOH slot-waveguide modulators

Effect of Zero Modes on the Bound-State Spectrum in Light-Cone Quantisation

We study the role of bosonic zero modes in light-cone quantisation on the invariant mass spectrum for the simplified setting of two-dimensional SU(2) Yang-Mills theory coupled to massive scalar adjoint matter. Specifically, we use discretised light-cone quantisation where the momentum modes become discrete. Two types of zero momentum mode appear -- constrained and dynamical zero modes. In fact only the latter type of modes turn out to mix with the Fock vacuum. Omission of the constrained modes leads to the dynamical zero modes being controlled by an infinite square-well potential. We find that taking into account the wavefunctions for these modes in the computation of the full bound state spectrum of the two dimensional theory leads to 21% shifts in the masses of the lowest lying states.Comment: LaTeX with 5 postscript file

Cytosine 5-Hydroxymethylation of the LZTS1 Gene Is Reduced in Breast Cancer

Change of DNA cytosine methylation (5mC) is an early event in the development of cancer, and the recent discovery of a 5-hydroxymethylated form (5hmC) of cytosine suggests a regulatory epigenetic role that might be different from 5-methylcytosine. Here, we aimed at elucidating the role of 5hmC in breast cancer. To interrogate the 5hmC levels of the leucine zipper, putative tumor suppressor 1 (LZTS1) gene in detail, we analyzed 75 primary breast cancer tissue samples from initial diagnosis and 12 normal breast tissue samples derived from healthy persons. Samples were subjected to 5hmC glucosyltransferase treatment followed by restriction digestion and segment-specific amplification of 11 polymerase chain reaction products. Nine of the 11 5′LZTS1 fragments showed significantly lower (fold change of 1.61–6.01, P < .05) 5hmC content in primary breast cancer tissue compared to normal breast tissue samples. No significant differences were observed for 5mC DNA methylation. Furthermore, both LZTS1 and TET1 mRNA expressions were significantly reduced in tumor samples (n = 75, P < .001, Student's t test), which correlated significantly with 5hmC levels in samples. 5hmC levels in breast cancer tissues were associated with unfavorable histopathologic parameters such as lymph node involvement (P < .05, Student's t test). A decrease of 5hmC levels of LZTS1, a classic tumor suppressor gene known to influence metastasis in breast cancer progression, is correlated to down-regulation of LZTS1 mRNA expression in breast cancer and might epigenetically enhance carcinogenesis. The study provides support for the novel hypothesis that suggests a strong influence of 5hmC on mRNA expression. Finally, one may also consider 5hmC as a new biomarker

Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition," editors Shyi-Ming Chen and Witold Pedrycz. The original publication is available at http://www.springerlink.co