Single shot phase contrast imaging using laser-produced Betatron x-ray beams

Development of x-ray phase contrast imaging applications with a laboratory scale source have been limited by the long exposure time needed to obtain one image. We demonstrate, using the Betatron x-ray radiation produced when electrons are accelerated and wiggled in the laser-wakefield cavity, that a high quality phase contrast image of a complex object (here, a bee), located in air, can be obtained with a single laser shot. The Betatron x-ray source used in this proof of principle experiment has a source diameter of 1.7 microns and produces a synchrotron spectrum with critical energy E_c=12.3 +- 2.5 keV and 10^9 photons per shot in the whole spectrum.Comment: 3 pages, 3 figure

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.C.E.B. was supported by a PhD fellowship from the EU-FP7 project EpiTrain (316758). J.H. was supported by the UCL Cancer Institute Research Trust. V.K.R. was supported by BLUEPRINT (282510). K.D. was funded as a HSST trainee by NHS Health Education England. M.F. was supported by the BHF Cambridge Centre of Excellence (RE/13/6/30180). Research in W.H.O.’s laboratory was supported by EU-FP7 project BLUEPRINT (282510) and by program grants from the National Institute for Health Research (NIHR, http://www.nihr.ac.uk) and the British Heart Foundation under numbers RP-PG-0310-1002 and RG/09/12/28096 (https://www.bhf.org.uk/). W.H.O.’s laboratory receives funding from NHS Blood and Transplant for facilities. We gratefully acknowledge the participation of all NIHR Cambridge BioResource volunteers. We thank the Cambridge BioResource staff for their help with volunteer recruitment. We thank members of the Cambridge BioResource SAB and Management Committee for their support of our study and the National Institute for Health Research Cambridge Biomedical Research Centre for funding. R.S. and his group were supported by the European Union in the framework of the BLUEPRINT Project (HEALTH-F5-2011-282510) and the German Ministry of Science and Education (BMBF) in the framework of the MMML-MYC-SYS project (036166B). We thank Deborah Winter (Weizmann Institute) for supplying a set of microglial enhancers from Lavin et al. (2014). Research in S.B.’s laboratory was supported by the Wellcome Trust (99148), Royal Society Wolfson Research Merit Award (WM100023), and EU-FP7 projects EpiTrain (316758), EPIGENESYS (257082), and BLUEPRINT (282510)

Experimental Investigation of Physics- and ML-based QoT Estimation for WDM Systems

With a seven-channel WDM transmission over 1000 km, we experimentally study the data-driven physics- and machine learning (ML)-based SNR estimation techniques. While the ML-based approach provides good estimation accuracy, the physics-based method performs close to it with more explainability and less training data requirements

Research data supporting "Experimental Investigation of Physics- and ML-based QoT Estimation for WDM Systems"

The Excel file contains four sheets and each of them associated with the figures 2 to 5 in the paper. The processed data was obtained by analising the captured 400 data each contains seven channel power launch into the fibre and corresponding SNR from the experimental setup described in the paper. Sheet for Fig.2 contains number of training data and corresponding root mean square error (RMSE) for channel 4 which is calculated from 50 test data for three methods based on physics, neural network (NN) and Gaussian process regression (GPR). Sheet for Fig.3 includes the data for RMSE [dB] of all seven channels for the three methods where number of training data for physics based method is 50 and ML-based method is 250. Maximum estimation error data for all seven channel for three methods is in the sheet for Fig.4. Finally, the sheet for Fig.5 contains back-to-back measured SNRs of seven channel and those estimated from physics-based method after 1000 km transmission.supported by the UK EPSRC for funding via the Programme Grant TRANSNET (EP/R035342/1). This research was performed under the auspices of a Ciena University collaborative research gran

Three-body dissociation dynamics of the low-lying Rydberg states of H-3 and D-3

The dynamics of the three-body dissociative charge exchange of fast (12 keV) H-3(+) and D-3(+) with Cs have been studied using multiparticle translational spectroscopy. The observed partitioning of product momenta was found to be state-specific and yields insights into the nuclear motion during dissociation for the three lowest-lying 2s (2)A(1)', 2p (2)A(2)", and 3p E-2' metastable Rydberg states of H-3 and the 2s (2)A(1)' and 2p (2)A(2)" states for D-3. These results provide direct empirical information on the nonadiabatic couplings that govern the three-body dissociation of the lowest-lying Rydberg states of H-3 and D-3

Physics-based Modeling for Hybrid Data-Driven Models to Estimate SNR in WDM Systems

Recently several machine learning methods have been proposed to estimate the SNR, based on launch data and other system factors. These data-driven methods typically require a large number of datasets for training and generally are not interpretable. In this paper, we propose an alternative approach that requires less data and is interpretable, specifically a hybrid algorithm combining a physical model with Gaussian process regression. We develop a measurement-informed physical model, systematically reducing the number of independent parameters based on the underpinning physics and improve the overall performance of the physical model marginally. The model is validated using measurements performed on a 15-channel wavelength-division multiplexed system propagating over 1,000 km of standard single-mode fiber. The proposed hybrid model is not only interpretable but also obtains better agreement with measurements than a Gaussian process regression model and a simple neural network model for a given number of training datapoints.Ciena and UK EPSRC TRANSNET project (EP/R035342/1