A generative model for natural sounds based on latent force modelling

Generative models based on subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitude modulation to be a crucial component of auditory perception. Probabilistic latent variable analysis can be particularly insightful, but existing approaches don’t incorporate prior knowledge about the physical behaviour of amplitude envelopes, such as exponential decay or feedback. We use latent force modelling, a probabilistic learning paradigm that encodes physical knowledge into Gaussian process regression, to model correlation across spectral subband envelopes. We augment the standard latent force model approach by explicitly modelling dependencies across multiple time steps. Incorporating this prior knowledge strengthens the interpretation of the latent functions as the source that generated the signal. We examine this interpretation via an experiment showing that sounds generated by sampling from our probabilistic model are perceived to be more realistic than those generated by comparative models based on nonnegative matrix factorisation, even in cases where our model is outperformed from a reconstruction error perspective

Anaplastic lymphoma kinase (ALK) inhibitor response in neuroblastoma is highly correlated with ALK mutation status, ALK mRNA and protein levels

Background In pediatric neuroblastoma (NBL), high anaplastic lymphoma kinase (ALK) levels appear to be correlated with an unfavorable prognosis, regardless of ALK mutation status. This suggests a therapeutic role for ALK inhibitors in NBL patients. We examined the correlation between levels of ALK, phosphorylated ALK (pALK) and downstream signaling proteins and response to ALK inhibition in a large panel of both ALK mutated and wild type (WT) NBL cell lines. Methods We measured protein levels by western blot and ALK inhibitor sensitivity (TAE684) by viability assays in 19 NBL cell lines of which 6 had a point mutation and 4 an amplification of the ALK gene. Results ALK 220 kDa (p=0.01) and ALK 140 kDa (p= 0.03) protein levels were higher in ALK mutant than WT cell lines. Response to ALK inhibition was significantly correlated with ALK protein levels (p<0.01). ALK mutant cell lines (n=4) were 14,9 fold (p<0,01) more sensitive to ALK inhibition than eight WT cell lines. Conclusion NBL cell lines often express ALK at high levels and are responsive to ALK inhibitors. Mutated cell lines express ALK at higher levels, which may define their superior response to ALK inhibition

Fatal myocarditis in a child with systemic onset juvenile idiopathic arthritis during treatment with an interleukin 1 receptor antagonist

<p>Abstract</p> <p>Background</p> <p>The pathologic diagnosis of isolated myocarditis without pericardial involvement is uncommonly encountered in systemic onset Juvenile Idiopathic Arthritis (soJIA).</p> <p>Case</p> <p>An eleven year-old boy with soJIA died suddenly while being treated with the interleukin 1 (IL-1) receptor inhibitor, anakinra. His autopsy revealed an enlarged heart and microscopic findings were consistent with myocarditis, but not pericarditis. Viral PCR testing performed on his myocardial tissue was negative.</p> <p>Conclusion</p> <p>This case illustrates myocarditis as a fatal complication of soJIA, potentially enabled by anakinra.</p

The solid-state photo-CIDNP effect

The solid-state photo-CIDNP effect is the occurrence of a non-Boltzmann nuclear spin polarization in rigid samples upon illumination. For solid-state NMR, which can detect this enhanced nuclear polarization as a strong modification of signal intensity, the effect allows for new classes of experiments. Currently, the photo- and spin-chemical machinery of various RCs is studied by photo-CIDNP MAS NMR in detail. Until now, the effect has only been observed at high magnetic fields with 13C and 15N MAS NMR and in natural photosynthetic RC preparations in which blocking of the acceptor leads to cyclic electron transfer. In terms of irreversible thermodynamics, the high-order spin structure of the initial radical pair can be considered as a transient order phenomenon emerging under non-equilibrium conditions and as a first manifestation of order in the photosynthetic process. The solid-state photo-CIDNP effect appears to be an intrinsic property of natural RCs. The conditions of its occurrence seem to be conserved in evolution. The effect may be based on the same fundamental principles as the highly optimized electron transfer. Hence, the effect may allow for guiding artificial photosynthesis

Self-assembled monolayer of light-harvesting core complexes of photosynthetic bacteria on an amino-terminated ITO electrode

Light-harvesting antenna core (LH1-RC) complexes isolated from Rhodospirillum rubrum and Rhodopseudomonas palustris were successfully self-assembled on an ITO electrode modified with 3-aminopropyltriethoxysilane. Near infra-red (NIR) absorption, fluorescence, and IR spectra of these LH1-RC complexes indicated that these LH1-RC complexes on the electrode were stable on the electrode. An efficient energy transfer and photocurrent responses of these LH1-RC complexes on the electrode were observed upon illumination of the LH1 complex at 880 nm

Physics of Neutron Star Crusts

The physics of neutron star crusts is vast, involving many different research fields, from nuclear and condensed matter physics to general relativity. This review summarizes the progress, which has been achieved over the last few years, in modeling neutron star crusts, both at the microscopic and macroscopic levels. The confrontation of these theoretical models with observations is also briefly discussed.Comment: 182 pages, published version available at <http://www.livingreviews.org/lrr-2008-10

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies