Characterising encapsulated nuclear waste using cosmic-ray muon tomography

Tomographic imaging techniques using the Coulomb scattering of cosmic-ray muons have been shown previously to successfully identify and characterise low- and high-Z materials within an air matrix using a prototype scintillating-fibre tracker system. Those studies were performed as the first in a series to assess the feasibility of this technology and image reconstruction techniques in characterising the potential high-Z contents of legacy nuclear waste containers for the UK Nuclear Industry. The present work continues the feasibility study and presents the first images reconstructed from experimental data collected using this small-scale prototype system of low- and high-Z materials encapsulated within a concrete-filled stainless-steel container. Clear discrimination is observed between the thick steel casing, the concrete matrix and the sample materials assayed. These reconstructed objects are presented and discussed in detail alongside the implications for future industrial scenarios.Comment: 6 pages, 4 figure

Design, synthesis and antibacterial activity of minor groove binders: the role of non-cationic tail groups

he design and synthesis of a new class of minor groove binder (MGBs) in which, the cationic tail group has been replaced by a neutral, polar variant including cyanoguanidine, nitroalkene, and trifluoroacetamide groups. Antibacterial activity (against Gram positive bacteria) was found for both the nitroalkene and trifluoroacetamide groups. For the case of the nitroalkene tail group, strong binding of a minor groove binder containing this tail group was demonstrated by both DNA footprinting and melting temperature measurements, showing a correlation between DNA binding and antibacterial activity. The compounds have also been evaluated for binding to the hERG ion channel to determine whether non-cationic but polar substituents might have an advantage compared with conventional cationic tail groups in avoiding hERG binding. In this series of compounds, it was found that whilst non-cationic compounds generally had lower affinity to the hERG ion channel, all of the compounds studied bound weakly to the hERG ion channel, probably associated with the hydrophobic head groups

Simulation of Neptunium Extraction in an Advanced PUREX Process — Model Improvement

Routing neptunium to a single product in spent nuclear fuel reprocessing is a significant challenge. In this work, we have further improved the simulation of neptunium extraction in an advanced PUREX flowsheet by applying a revised model of the Np(V)-Np(VI) redox reaction kinetics, a new nitric acid radiolysis model and by evaluating various models for the nitrous acid distribution coefficient. The Np disproportionation reaction is shown to have a negligible effect. The models are validated against published ‘cold test’ experimental results; the ‘hot test’ simulation suggests high neptunium radiolysis could help to achieve high recoveries using this flowsheet

Cellular Models of Aggregation-Dependent Template-Directed Proteolysis to Characterize Tau Aggregation Inhibitors for Treatment of Alzheimer's Disease

Copyright © 2015, The American Society for Biochemistry and Molecular Biology. Acknowledgements-We thank Drs Timo Rager and Rolf Hilfiker (Solvias, Switzerland) for polymorph analyses.Peer reviewedPublisher PD

Structural modeling of an outer membrane electron conduit from a metal-reducing bacterium suggests electron transfer via periplasmic redox partners

Many subsurface microorganisms couple their metabolism to the reduction or oxidation of extracellular substrates. For example, anaerobic mineral-respiring bacteria can use external metal oxides as terminal electron acceptors during respiration. Porin–cytochrome complexes facilitate the movement of electrons generated through intracellular catabolic processes across the bacterial outer membrane to these terminal electron acceptors. In the mineral-reducing model bacterium Shewanella oneidensis MR-1, this complex is composed of two decaheme cytochromes (MtrA and MtrC) and an outer-membrane β-barrel (MtrB). However, the structures and mechanisms by which porin–cytochrome complexes transfer electrons are unknown. Here, we used small-angle neutron scattering (SANS) to study the molecular structure of the transmembrane complexes MtrAB and MtrCAB. Ab initio modeling of the scattering data yielded a molecular envelope with dimensions of ∼105 × 60 × 35 Å for MtrAB and ∼170 × 60 × 45 Å for MtrCAB. The shapes of these molecular envelopes suggested that MtrC interacts with the surface of MtrAB, extending ∼70 Å from the membrane surface and allowing the terminal hemes to interact with both MtrAB and an extracellular acceptor. The data also reveal that MtrA fully extends through the length of MtrB, with ∼30 Å being exposed into the periplasm. Proteoliposome models containing membrane-associated MtrCAB and internalized small tetraheme cytochrome (STC) indicate that MtrCAB could reduce Fe(III) citrate with STC as an electron donor, disclosing a direct interaction between MtrCAB and STC. Taken together, both structural and proteoliposome experiments support porin–cytochrome–mediated electron transfer via periplasmic cytochromes such as STC

MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.Comment: ISCA 202