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

Light‐Activated Electron Transfer and Catalytic Mechanism of Carnitine Oxidation by Rieske‐Type Oxygenase from Human Microbiota

Oxidation of quaternary ammonium substrate, carnitine by non-heme iron containing Acinetobacter baumannii (Ab) oxygenase CntA/reductase CntB is implicated in the onset of human cardiovascular disease. Herein, we develop a blue-light (365 nm) activation of NADH coupled to electron paramagnetic resonance (EPR) measurements to study electron transfer from the excited state of NADH to the oxidized, Rieske-type, [2Fe-2S]2+ cluster in the AbCntA oxygenase domain with and without the substrate, carnitine. Further electron transfer from one-electron reduced, Rieske-type [2Fe-2S]1+ center in AbCntA-WT to the mono-nuclear, non-heme iron center through the bridging glutamate E205 and subsequent catalysis occurs only in the presence of carnitine. The electron transfer process in the AbCntA-E205A mutant is severely affected, which likely accounts for the significant loss of catalytic activity in the AbCntA-E205A mutant. The NADH photo-activation coupled with EPR is broadly applicable to trap reactive intermediates at low temperature and creates a new method to characterize elusive intermediates in multiple redox-centre containing proteins

Light‐Activated Electron Transfer and Catalytic Mechanism of Carnitine Oxidation by Rieske‐Type Oxygenase from Human Microbiota

Oxidation of quaternary ammonium substrate, carnitine by non-heme iron containing Acinetobacter baumannii (Ab) oxygenase CntA/reductase CntB is implicated in the onset of human cardiovascular disease. Herein, we develop a blue-light (365 nm) activation of NADH coupled to electron paramagnetic resonance (EPR) measurements to study electron transfer from the excited state of NADH to the oxidized, Rieske-type, [2Fe-2S]2+ cluster in the AbCntA oxygenase domain with and without the substrate, carnitine. Further electron transfer from one-electron reduced, Rieske-type [2Fe-2S]1+ center in AbCntA-WT to the mono-nuclear, non-heme iron center through the bridging glutamate E205 and subsequent catalysis occurs only in the presence of carnitine. The electron transfer process in the AbCntA-E205A mutant is severely affected, which likely accounts for the significant loss of catalytic activity in the AbCntA-E205A mutant. The NADH photo-activation coupled with EPR is broadly applicable to trap reactive intermediates at low temperature and creates a new method to characterize elusive intermediates in multiple redox-centre containing proteins

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

First Observation of the Hadronic Transition Υ(4S)→ηhb(1P)and New Measurement of the hb(1P) and ηb(1S) Parameters

Using a sample of 771.6×106 ΥΥ(4S) decays collected by the Belle experiment at the KEKB e+e− collider, we observe, for the first time, the transition Υ(4S)→ηhb(1P) with the branching fraction B[Υ(4S)→ηhb(1P)]=(2.18±0.11±0.18)×10−3 and we measure the hb(1P) mass Mhb(1P)=(9899.3±0.4±1.0)  MeV/c2, corresponding to the hyperfine (HF) splitting ΔMHF(1P)=(0.6±0.4±1.0)  MeV/c2. Using the transition hb(1P)→γηb(1S), we measure the ηb(1S) mass Mηb(1S)=(9400.7±1.7±1.6)  MeV/c2, corresponding to ΔMHF(1S)=(59.6±1.7±1.6)  MeV/c2, the ηb(1S) width Γηb(1S)=(8+6−5±5)  MeV/c2and the branching fraction B[hb(1P)→γηb(1S)]=(56±8±4)%

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

The new scintillating fiber detector of E835 at Fermilab

Abstract The scintillating fiber tracker for the measurement of the polar coordinate θ for experiment E835 at Fermilab has been upgraded, by adding two extra layers (240 fibers each), at R ≈9 cm from the beam axis. Photons from the fibers are detected by Visible Light Photon Counters (VLPCs). The high granularity, flexibility and fast response of the scintillating fibers, combined with the high quantum efficiency of the VLPCs, allow high rate capability, high efficiency, good tracking and time resolution. Signals from the outer two layers are used to provide θ information to the first-level trigger

Tactile Proprioceptive Input in Robotic Rehabilitation after Stroke

Stroke can lead to loss or impairment of somatosensory sensation (i.e. proprioception), that reduces functional control of limb movements. Here we examine the possibility of providing artificial feedback to make up for lost sensory information following stroke. However, it is not clear whether this kind of sensory substitution is even possible due to stroke-related loss of central processing pathways that subserve somatosensation. In this paper we address this issue in a small cohort of stroke survivors using a tracking task that emulates many activities of daily living. Artificial proprioceptive information was provided to the subjects in the form of vibrotactile cues. The goal was to assist participants in guiding their arm towards a moving target on the screen. Our experiment indicates reliable tracking accuracy under the effect of vibrotactile proprioceptive feedback, even in subjects with impaired natural proprioception. This result is promising and can create new directions in rehabilitation robotics with augmented somatosensory feedback