Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

International audienceIn this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download

Predicting the mechanism of phospholipidosis.

The mechanism of phospholipidosis is still not well understood. Numerous different mechanisms have been proposed, varying from direct inhibition of the breakdown of phospholipids to the binding of a drug compound to the phospholipid, preventing breakdown. We have used a probabilistic method, the Parzen-Rosenblatt Window approach, to build a model from the ChEMBL dataset which can predict from a compound's structure both its primary pharmaceutical target and other targets with which it forms off-target, usually weaker, interactions. Using a small dataset of 182 phospholipidosis-inducing and non-inducing compounds, we predict their off-target activity against targets which could relate to phospholipidosis as a side-effect of a drug. We link these targets to specific mechanisms of inducing this lysosomal build-up of phospholipids in cells. Thus, we show that the induction of phospholipidosis is likely to occur by separate mechanisms when triggered by different cationic amphiphilic drugs. We find that both inhibition of phospholipase activity and enhanced cholesterol biosynthesis are likely to be important mechanisms. Furthermore, we provide evidence suggesting four specific protein targets. Sphingomyelin phosphodiesterase, phospholipase A2 and lysosomal phospholipase A1 are shown to be likely targets for the induction of phospholipidosis by inhibition of phospholipase activity, while lanosterol synthase is predicted to be associated with phospholipidosis being induced by enhanced cholesterol biosynthesis. This analysis provides the impetus for further experimental tests of these hypotheses.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers.

BACKGROUND: The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository. RESULTS: It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively. CONCLUSIONS: 2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.The authors would like to thank Unilever for funding. We thank Dr. Guus Duchateau, Leo van Buren and Prof. Werner Klaffke for useful discussions in the development of this work.This is the final version. It was first published by Chemistry Central at http://www.jcheminf.com/content/6/1/29

Using neural networks to describe tracer correlations

Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH₄-N₂O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH₄-N₂O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH_4  (but not N₂O) from 1991 till the present. The neural network Fortran code used is available for download

Simple broadband circularly polarized monopole antenna with two asymmetrically connected U-shaped parasitic strips and defective ground plane

A simple compact broadband circularly polarized monopole antenna, which comprises a simple monopole, a modified ground plane with an implementing triangular stub and two asymmetrically connected U-shaped parasitic strips, is proposed. Simulation results show that the proposed compact antenna (0.62λo×0.68λo) achieves a 10-dB impedance bandwidth (IBW) of 111% (1.7 to 5.95 GHz) and a 3-dB axial ratio bandwidth (ARBW) of 61% (3.3–6.2 GHz) with a peak gain between 2.9–4 dBi for the entire ARBW. With its broad IBW and ARBW, compact size and simple structure, the proposed antenna is suitable for different wireless communications

Evidence for a new resonance and search for the Y(4140) in γγ→ϕJ/ψ\gamma \gamma \to \phi J/\psi

The process \gamma \gamma \to \phi \jpsi is measured for \phi \jpsi masses between threshold and 5 GeV/${\it c}^2$, using a data sample of 825 fb$^{-1}$ collected with the Belle detector. A narrow peak of $8.8^{+4.2}_{-3.2}$ events, with a significance of 3.2 standard deviations including systematic uncertainty, is observed. The mass and natural width of the structure (named X(4350)) are measured to be $(4350.6^{+4.6}_{-5.1}(\rm{stat})\pm 0.7(\rm{syst})) \hbox{MeV}/{\it c}^2$ and $(13^{+18}_{-9}(\rm{stat})\pm 4(\rm{syst})) \hbox{MeV}$, respectively. The product of its two-photon decay width and branching fraction to \phi\jpsi is $(6.7^{+3.2}_{-2.4}(\rm{stat}) \pm 1.1(\rm{syst})) \hbox{eV}$ for $J^P=0^+$, or $(1.5^{+0.7}_{-0.6}(\rm{stat}) \pm 0.3(\rm{syst})) \hbox{eV}$ for $J^P=2^+$. No signal for the Y(4140)\to \phi \jpsi structure reported by the CDF Collaboration in B\to K^+ \phi \jpsi decays is observed, and limits of \Gamma_{\gamma \gamma}(Y(4140)) \BR(Y(4140)\to\phi \jpsi)<41 \hbox{eV} for $J^P=0^+$ or $<6.0 \hbox{eV}$ for $J^P=2^+$ are determined at the 90% C.L. This disfavors the scenario in which the Y(4140) is a $D_{s}^{\ast+} {D}_{s}^{\ast-}$ molecule.Comment: 9 pages, 3 figures, publication in Phys. Rev. Lett. 104, 112004, 201

Observation of X(3872)→J/ψγX(3872)\to J/\psi \gamma and search for X(3872)→ψ′γX(3872)\to\psi'\gamma in B decays

We report a study of $B\to (J/\psi \gamma) K$ and $B\to (\psi' \gamma)K$ decay modes using $772\times 10^{6}$ $B\bar{B}$ events collected at the \Upsilon(4S)$resonance with the Belle detector at the KEKB energy-asymmetric$e^+ e^-$collider. We observe$X(3872) \to J/\psi \gamma$and report the first evidence for$\chi_{c2} \to J/\psi \gamma$in$B\to (X_{c\bar{c}}\gamma) K$decays, while in a search for$X(3872) \to \psi' \gamma$no significant signal is found. We measure the branching fractions,$\mathcal{B}(B^{\pm} \to X(3872) K^{\pm}) \mathcal{B}(X(3872) \to J/\psi\gamma)$$=$$(1.78^{+0.48}_{-0.44}\pm 0.12)\times 10^{-6}$,$\mathcal{B} (B^{\pm} \to\chi_{c2} K^{\pm})$$=$$(1.11^{+0.36}_{-0.34} \pm 0.09) \times 10^{-5}$,$\mathcal{B}(B^{\pm} \to X(3872) K^{\pm}) \mathcal{B}(X(3872) \to \psi'\gamma)$$<$$3.45\times 10^{-6}$ (upper limit at 90% C.L.) and also provide upper limits for other searches.Comment: 6 pages, 3 figure