Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes.

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification

Memristive operation mode of a site-controlled quantum dot floating gate transistor

The authors gratefully acknowledge financial support from the European Union (FPVII (2007-2013) under Grant Agreement No. 318287 Landauer) as well as the state of Bavaria.We have realized a floating gate transistor based on a GaAs/AlGaAs heterostructure with site-controlled InAs quantum dots. By short-circuiting the source contact with the lateral gates and performing closed voltage sweep cycles, we observe a memristive operation mode with pinched hysteresis loops and two clearly distinguishable conductive states. The conductance depends on the quantum dot charge which can be altered in a controllable manner by the voltage value and time interval spent in the charging region. The quantum dot memristor has the potential to realize artificial synapses in a state-of-the-art opto-electronic semiconductor platform by charge localization and Coulomb coupling.Publisher PDFPeer reviewe

Event-driven simulations of a plastic, spiking neural network

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.Comment: 9 pages, 6 figure

Neutrino emission from dark matter annihilation/decay in light of cosmic e±e^{\pm} and pˉ\bar{p} data

A self-consistent global fitting method based on the Markov Chain Monte Carlo technique to study the dark matter (DM) property associated with the cosmic ray electron/positron excesses was developed in our previous work. In this work we further improve the previous study to include the hadronic branching ratio of DM annihilation/decay. The PAMELA $\bar{p}/p$ data are employed to constrain the hadronic branching ratio. We find that the 95% ($2\sigma$) upper limits of the quark branching ratio allowed by the PAMELA $\bar{p}/p$ data is $\sim 0.032$ for DM annihilation and $\sim 0.044$ for DM decay respectively. This result shows that the DM coupling to pure leptons is indeed favored by the current data. Based on the global fitting results, we further study the neutrino emission from DM in the Galactic center. Our predicted neutrino flux is some smaller than previous works since the constraint from $\gamma$-rays is involved. However, it is still capable to be detected by the forth-coming neutrino detector such as IceCube. The improved points of the present study compared with previous works include: 1) the DM parameters, both the particle physical ones and astrophysical ones, are derived in a global fitting way, 2) constraints from various species of data sets, including $\gamma$-rays and antiprotons are included, and 3) the expectation of neutrino emission is fully self-consistent.Comment: 13 pages, 2 figures, 1 table; Published in IJMPA 201

Micro-attention for micro-expression recognition

Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression

Epigenetic Chromatin Silencing: Bistability and Front Propagation

The role of post-translational modification of histones in eukaryotic gene regulation is well recognized. Epigenetic silencing of genes via heritable chromatin modifications plays a major role in cell fate specification in higher organisms. We formulate a coarse-grained model of chromatin silencing in yeast and study the conditions under which the system becomes bistable, allowing for different epigenetic states. We also study the dynamics of the boundary between the two locally stable states of chromatin: silenced and unsilenced. The model could be of use in guiding the discussion on chromatin silencing in general. In the context of silencing in budding yeast, it helps us understand the phenotype of various mutants, some of which may be non-trivial to see without the help of a mathematical model. One such example is a mutation that reduces the rate of background acetylation of particular histone side-chains that competes with the deacetylation by Sir2p. The resulting negative feedback due to a Sir protein depletion effect gives rise to interesting counter-intuitive consequences. Our mathematical analysis brings forth the different dynamical behaviors possible within the same molecular model and guides the formulation of more refined hypotheses that could be addressed experimentally.Comment: 19 pages, 5 figure

Cosmological Evolution of Interacting Dark Energy Models with Mass Varying Neutrinos

In this paper we consider the cosmological implications of dark energy models with a coupled system of a dynamical scalar field (the quintessence) and the neutrinos. By detailed numerical calculations we study the various possibilities on the evolution and the fates of the universe in this class of models. Our results show that due to the interaction with quintessence, neutrinos could be dominant over the quintessence in the future universe, however would eventually decay away.Comment: One typographical error corrected, references updated and presentation improve

Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

A theory of temporally asymmetric Hebb (TAH) rules which depress or potentiate synapses depending upon whether the postsynaptic cell fires before or after the presynaptic one is presented. Using the Fokker-Planck formalism, we show that the equilibrium synaptic distribution induced by such rules is highly sensitive to the manner in which bounds on the allowed range of synaptic values are imposed. In a biologically plausible multiplicative model, we find that the synapses in asynchronous networks reach a distribution that is invariant to the firing rates of either the pre- or post-synaptic cells. When these cells are temporally correlated, the synaptic strength varies smoothly with the degree and phase of synchrony between the cells.Comment: 3 figures, minor corrections of equations and tex