Unsupervised vector-based classification of single-molecule charge transport data

The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail

Chronicling Stankonia The Rise of the Hip Hop South Book Review

Book review of Chronicling Stankonia The Rise of the Hip Hop Sout

Improving lightly supervised training for broadcast transcription

This paper investigates improving lightly supervised acoustic model training for an archive of broadcast data. Standard lightly supervised training uses automatically derived decoding hypotheses using a biased language model. However, as the actual speech can deviate significantly from the original programme scripts that are supplied, the quality of standard lightly supervised hypotheses can be poor. To address this issue, word and segment level combination approaches are used between the lightly supervised transcripts and the original programme scripts which yield improved transcriptions. Experimental results show that systems trained using these improved transcriptions consistently outperform those trained using only the original lightly supervised decoding hypotheses. This is shown to be the case for both the maximum likelihood and minimum phone error trained systems.The research leading to these results was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology).This is the accepted manuscript version. The final version is available at http://www.isca-speech.org/archive/interspeech_2013/i13_2187.html

Computational modelling of emboli travel trajectories in cerebral arteries: Influence of microembolic particle size and density

This article has been made available through the Brunel Open Access Publishing Fund.Ischaemic stroke is responsible for up to 80 % of stroke cases. Prevention of the reoccurrence of ischaemic attack or stroke for patients who survived the first symptoms is the major treatment target. Accurate diagnosis of the emboli source for a specific infarction lesion is very important for a better treatment for the patient. However, due to the complex blood flow patterns in the cerebral arterial network, little is known so far of the embolic particle flow trajectory and its behaviour in such a complex flow field. The present study aims to study the trajectories of embolic particles released from carotid arteries and basilar artery in a cerebral arterial network and the influence of particle size, mass and release location to the particle distributions, by computational modelling. The cerebral arterial network model, which includes major arteries in the circle of Willis and several generations of branches from them, was generated from MRI images. Particles with diameters of 200, 500 and 800 μ m and densities of 800, 1,030 and 1,300 kg/m 3 were released in the vessel's central and near-wall regions. A fully coupled scheme of particle and blood flow in a computational fluid dynamics software ANASYS CFX 13 was used in the simulations. The results show that heavy particles (density large than blood or a diameter larger than 500 μ m) normally have small travel speeds in arteries; larger or lighter embolic particles are more likely to travel to large branches in cerebral arteries. In certain cases, all large particles go to the middle cerebral arteries; large particles with higher travel speeds in large arteries are likely to travel at more complex and tortuous trajectories; emboli raised from the basilar artery will only exit the model from branches of basilar artery and posterior cerebral arteries. A modified Circle of Willis configuration can have significant influence on particle distributions. The local branch patterns of internal carotid artery to middle cerebral artery and anterior communicating artery can have large impact on such distributions. © 2014 The Author(s)

The diagnosis of posttraumatic stress disorder in school-aged children and adolescents following pediatric intensive care unit admission

Objectives: This study explored the diagnosis of posttraumatic stress disorder (PTSD) in children and adolescents following pediatric intensive care unit (PICU) admission. Specifically, the study aimed to describe the presentation and prevalence of PTSD symptoms 6 months postdischarge, explore the validity of the DSM-IV PTSD algorithm and alternative PTSD algorithm (PTSD-AA) in school-aged children and adolescents, and examine the diagnostic utility of Criterion C3 (inability to recall aspects of a trauma) in this cohort

Re: Response to Request for Relevant Information on Bisphenol A Dear Ms. Oshita,

Authoritative Bodies Mechanism: Bisphenol-A). The Polycarbonate/BPA Global Group consists of the leading global manufacturers of bisphenol A and polycarbonate plastic, which for many years have supported and conducted scientific research to understand whether bisphenol A has the potential to cause health or environmental effects and to support scientifically sound public policy. As indicated by the signatures at the end of the attachment, the comments were prepare

Efficient Bayesian-based Multi-View Deconvolution

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the sam- ples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a Bayesian- based derivation of multi-view deconvolution that drastically improves the convergence time and provide a fast implementation utilizing graphics hardware.Comment: 48 pages, 20 figures, 1 table, under review at Nature Method