Detection of Dark Matter Concentrations in the Field of Cl 1604+4304 from Weak Lensing Analysis

We present a weak-lensing analysis of a region around the galaxy cluster Cl 1604+4304 (z=0.897) on the basis of the deep observations with the HST/WFPC2. We apply a variant of Schneider's aperture mass technique to the observed WFPC2 field and obtain the distribution of weak-lensing signal-to-noise (S/N) ratio within the field. The resulting S/N map reveals a clear pronounced peak located about 1.7 arcmin (850h_{50}^{-1} kpc at z=0.897) southwest of the second peak associated with the optical cluster center determined from the dynamical analysis of Postman et al. A non-linear finite-field inversion method has been used to reconstruct the projected mass distribution from the observed shear field. The reconstructed mass map shows a super-critical feature at the location of the S/N peak as well as in the cluster central region. Assuming the redshift distribution of field galaxies, we obtain the total mass in the observed field to be 1.0 h_{50}^{-1} 10^{15} M_sun for =1.0. The estimated mass within a circular aperture of radius 280h_{50}^{-1} kpc centered on the dark clump is 2.4h_{50}^{-1} 10^{14} M_sun. We have confirmed the existence of the ` dark ' mass concentration from another deep HST observation with a slightly different ~20 arcsec pointing.Comment: 7 pages, 3 figure

Task-Irrelevant Perceptual Learning Specific to the Contrast Polarity of Motion Stimuli

Studies of perceptual learning have focused on aspects of learning that are related to early stages of sensory processing. However, conclusions that perceptual learning results in low-level sensory plasticity are of great controversy, largely because such learning can often be attributed to plasticity in later stages of sensory processing or in the decision processes. To address this controversy, we developed a novel random dot motion (RDM) stimulus to target motion cells selective to contrast polarity, by ensuring the motion direction information arises only from signal dot onsets and not their offsets, and used these stimuli in conjunction with the paradigm of task-irrelevant perceptual learning (TIPL). In TIPL, learning is achieved in response to a stimulus by subliminally pairing that stimulus with the targets of an unrelated training task. In this manner, we are able to probe learning for an aspect of motion processing thought to be a function of directional V1 simple cells with a learning procedure that dissociates the learned stimulus from the decision processes relevant to the training task. Our results show learning for the exposed contrast polarity and that this learning does not transfer to the unexposed contrast polarity. These results suggest that TIPL for motion stimuli may occur at the stage of directional V1 simple cells.CELEST, an NSF Science of Learning Center (SBE-0354378); Defense Advanced Research Projects Agency SyNAPSE program (HR0011-09-3-0001, HR001-09-C-0011); National Science Foundation (BCS-0549036); National Institutes of Health (R21 EY017737

Summer Residency of Pacific Halibut in Glacier Bay National Park

Glacier Bay National Park (Fig.1), as a Marine Protected Area (MPA), is phasing out commercial fishing of Pacific halibut (Hippoglossus stenolepis) within the park. The species continues to be commercially harvested outside of the bay

Ionic conductivity and relaxation dynamics in plastic-crystals with nearly globular molecules

We have performed a dielectric investigation of the ionic charge transport and the relaxation dynamics in plastic-crystalline 1-cyano-adamantane (CNA) and in two mixtures of CNA with the related plastic crystals adamantane or 2-adamantanon. Ionic charge carriers were provided by adding 1% of Li salt. The molecules of these compounds have nearly globular shape and, thus, the so-called revolving-door mechanism assumed to promote ionic charge transport via molecular reorientations in other PC electrolytes, should not be active here. Indeed, a comparison of the dc resistivity and the reorientational alpha-relaxation times in the investigated PCs, reveals complete decoupling of both dynamics. Similar to other PCs, we find a significant mixing-induced enhancement of the ionic conductivity. Finally, these solid-state electrolytes reveal a second relaxation process, slower than the alpha-relaxation, which is related to ionic hopping. Due to the mentioned decoupling, it can be unequivocally detected and is not superimposed by the reorientational contributions as found for most other ionic conductors.Comment: 9 pages, 7 figure

PYRO-NN: Python Reconstruction Operators in Neural Networks

Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the CT reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches are forced to use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan- and cone-beam projectors and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high level Python API allows a simple use of the layers as known from Tensorflow. To demonstrate the capabilities of the layers, the framework comes with three baseline experiments showing a cone-beam short scan FDK reconstruction, a CT reconstruction filter learning setup, and a TV regularized iterative reconstruction. All algorithms and tools are referenced to a scientific publication and are compared to existing non deep learning reconstruction frameworks. The framework is available as open-source software at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step towards reproducible researchComment: V1: Submitted to Medical Physics, 11 pages, 7 figure

Risk factors and mechanisms of hepatocarcinogenesis with special emphasis on alcohol and oxidative stress

Hepatocellular cancer is the fifth most frequent cancer in men and the eighth in women worldwide. Established risk factors are chronic hepatitis B and C infection, chronic heavy alcohol consumption, obesity and type 2 diabetes, tobacco use, use of oral contraceptives, and aflatoxin-contaminated food. Almost 90% of all hepatocellular carcinomas develop in cirrhotic livers. In Western countries, attributable risks are highest for cirrhosis due to chronic alcohol abuse and viral hepatitis B and C infection. Among those with alcoholic cirrhosis, the annual incidence of hepatocellular cancer is 1-2%. An important mechanism implicated in alcohol-related hepatocarcinogenesis is oxidative stress from alcohol metabolism, inflammation, and increased iron storage. Ethanol-induced cytochrome P-450 2E1 produces various reactive oxygen species, leading to the formation of lipid peroxides such as 4-hydroxy-nonenal. Furthermore, alcohol impairs the antioxidant defense system, resulting in mitochondrial damage and apoptosis. Chronic alcohol exposure elicits hepatocyte hyperregeneration due to the activation of survival factors and interference with retinoid metabolism. Direct DNA damage results from acetaldehyde, which can bind to DNA, inhibit DNA repair systems, and lead to the formation of carcinogenic exocyclic DNA etheno adducts. Finally, chronic alcohol abuse interferes with methyl group transfer and may thereby alter gene expressio

Distance-Redshift in Inhomogeneous Omega0=1Omega_0=1 Friedmann-Lemaitre-Robertson-Walker Cosmology

Distance--redshift relations are given in terms of associated Legendre functions for partially filled beam observations inspatially flat Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmologies. These models are dynamically pressure-free, flat FLRW on large scales but, due to mass inhomogeneities, differ in their optical properties. The partially filled beam area-redshift equation is a Lame$^{\prime}$ equation for arbitrary FLRW and is shown to simplify to the associated Legendre equation for the spatially flat, i.e. $\Omega_0=1$ case. We fit these new analytic Hubble curves to recent supernovae (SNe) data in an attempt to determine both the mass parameter $\Omega_m$ and the beam filling parameter $\nu$. We find that current data are inadequate to limit $\nu$. However, we are able to estimate what limits are possible when the number of observed SNe is increased by factor of 10 or 100, sample sizes achievable in the near future with the proposed SuperNova Acceleration Probe satellite.Comment: 9 pages, 3 figure