Representation and use of chemistry in the global electronic age.

We present an overview of the current state of public semantic chemistry and propose new approaches at a strategic and a detailed level. We show by example how a model for a Chemical Semantic Web can be constructed using machine-processed data and information from journal articles.This manuscript addresses questions of robotic access to data and its automatic re-use, including the role of Open Access archival of data. This is a pre-refereed preprint allowed by the publisher's (Royal Soc. Chemistry) Green policy. The author's preferred manuscript is an HTML hyperdocument with ca. 20 links to images, some of which are JPEgs and some of which are SVG (scalable vector graphics) including animations. There are also links to molecules in CML, for which the Jmol viewer is recommended. We susgeest that readers who wish to see the full glory of the manuscript, download the Zipped version and unpack on their machine. We also supply a PDF and DOC (Word) version which obviously cannot show the animations, but which may be the best palce to start, particularly for those more interested in the text

Hierarchical strategies for efficient fault recovery on the reconfigurable PAnDA device

A novel hierarchical fault-tolerance methodology for reconfigurable devices is presented. A bespoke multi-reconfigurable FPGA architecture, the programmable analogue and digital array (PAnDA), is introduced allowing fine-grained reconfiguration beyond any other FPGA architecture currently in existence. Fault blind circuit repair strategies, which require no specific information of the nature or location of faults, are developed, exploiting architectural features of PAnDA. Two fault recovery techniques, stochastic and deterministic strategies, are proposed and results of each, as well as a comparison of the two, are presented. Both approaches are based on creating algorithms performing fine-grained hierarchical partial reconfiguration on faulty circuits in order to repair them. While the stochastic approach provides insights into feasibility of the method, the deterministic approach aims to generate optimal repair strategies for generic faults induced into a specific circuit. It is shown that both techniques successfully repair the benchmark circuits used after random faults are induced in random circuit locations, and the deterministic strategies are shown to operate efficiently and effectively after optimisation for a specific use case. The methods are shown to be generally applicable to any circuit on PAnDA, and to be straightforwardly customisable for any FPGA fabric providing some regularity and symmetry in its structure

Structural basis for complement factor H-linked age-related macular degeneration

This is the final version of the article. Available from the publisher via the DOI in this record.Nearly 50 million people worldwide suffer from age-related macular degeneration (AMD), which causes severe loss of central vision. A single-nucleotide polymorphism in the gene for the complement regulator factor H (FH), which causes a Tyr-to-His substitution at position 402, is linked to approximately 50% of attributable risks for AMD. We present the crystal structure of the region of FH containing the polymorphic amino acid His402 in complex with an analogue of the glycosaminoglycans (GAGs) that localize the complement regulator on the cell surface. The structure demonstrates direct coordination of ligand by the disease-associated polymorphic residue, providing a molecular explanation of the genetic observation. This glycan-binding site occupies the center of an extended interaction groove on the regulator's surface, implying multivalent binding of sulfated GAGs. This finding is confirmed by structure-based site-directed mutagenesis, nuclear magnetic resonance-monitored binding experiments performed for both H402 and Y402 variants with this and another model GAG, and analysis of an extended GAG-FH complex.B. Prosser is funded by the Wellcome Trust Structural Biology Training Program (075415/Z/04/Z). S. Johnson and P. Roversi were funded by grants to S.M. Lea from the Medical Research Council (MRC) of the United Kingdom (grants G0400389 and G0400775). D. Uhrin and P.N. Barlow were funded by the Wellcome Trust (078780/ Z/05/Z). S.J. Clark was funded by an MRC Doctoral Training Account (G78/7925), and R.B. Sim and A.J. Day were funded by MRC core funding to the MRC Immunochemistry Unit

A Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN

While state-of-the-art development in CNN topology, such as VGGNet and ResNet, have become increasingly accurate, these networks are computationally expensive involving billions of arithmetic operations and parameters. To improve the classification accuracy, state-of-the-art CNNs usually involve large and complex convolutional layers. However, for certain applications, e.g. Internet of Things (IoT), where such CNNs are to be implemented on resource-constrained platforms, the CNN architectures have to be small and efficient. To deal with this problem, reducing the resource consumption in convolutional layers has become one of the most significant solutions. In this work, a multi-objective optimisation approach is proposed to trade-off between the amount of computation and network accuracy by using Multi-Objective Evolutionary Algorithms (MOEAs). The number of convolution kernels and the size of these kernels are proportional to computational resource consumption of CNNs. Therefore, this paper considers optimising the computational resource consumption by reducing the size and number of kernels in convolutional layers. Additionally, the use of unconventional kernel shapes has been investigated and results show these clearly outperform the commonly used square convolution kernels. The main contributions of this paper are therefore a methodology to significantly reduce computational cost of CNNs, based on unconventional kernel shapes, and provide different trade-offs for specific use cases. The experimental results further demonstrate that the proposed method achieves large improvements in resource consumption with no significant reduction in network performance. Compared with the benchmark CNN, the best trade-off architecture shows a reduction in multiplications of up to 6X and with slight increase in classification accuracy on CIFAR-10 dataset.Comment: 13 pages paper, plus 17 papers supplementary material

Rapid Quantification of Molecular Diversity for Selective Database Acquisition

There is an increasing need to expand the structural diversity of the molecules investigated in lead-discovery programs. One way in which this can be achieved is by acquiring external datasets that will enhance an existing database. This paper describes a rapid procedure for the selection of external datasets using a measure of structural diversity that is calculated from sums of pairwise intermolecular structural similarities

Scholarly communication in transition: The use of question marks in the titles of scientific articles in medicine, life sciences and physics 1966–2005

The titles of scientific articles have a special significance. We examined nearly 20 million scientific articles and recorded the development of articles with a question mark at the end of their titles over the last 40 years. Our study was confined to the disciplines of physics, life sciences and medicine, where we found a significant increase from 50% to more than 200% in the number of articles with question-mark titles. We looked at the principle functions and structure of the titles of scientific papers, and we assume that marketing aspects are one of the decisive factors behind the growing usage of question-mark titles in scientific articles

Impact of centralising acute stroke services in English metropolitan areas on mortality and length of hospital stay: difference-in-differences analysis

Objective: To investigate whether centralisation of acute stroke services in two metropolitan areas of England was associated with changes in mortality and length of hospital stay. Design: Analysis of difference-in-differences between regions with patient level data from the hospital episode statistics database linked to mortality data supplied by the Office for National Statistics. Setting: Acute stroke services in Greater Manchester and London, England. Participants: 258 915 patients with stroke living in urban areas and admitted to hospital in January 2008 to March 2012. Interventions “Hub and spoke” model for acute stroke care. In London hyperacute care was provided to all patients with stroke. In Greater Manchester hyperacute care was provided to patients presenting within four hours of developing symptoms of stroke. Main outcome measures Mortality from any cause and at any place at 3, 30, and 90 days after hospital admission; length of hospital stay. Results: In London there was a significant decline in risk adjusted mortality at 3, 30, and 90 days after admission. At 90 days the absolute reduction was −1.1% (95% confidence interval −2.1 to −0.1; relative reduction 5%), indicating 168 fewer deaths (95% confidence interval 19 to 316) during the 21 month period after reconfiguration in London. In both areas there was a significant decline in risk adjusted length of hospital stay: −2.0 days in Greater Manchester (95% confidence interval −2.8 to −1.2; 9%) and −1.4 days in London (−2.3 to −0.5; 7%). Reductions in mortality and length of hospital stay were largely seen among patients with ischaemic stroke. Conclusions: A centralised model of acute stroke care, in which hyperacute care is provided to all patients with stroke across an entire metropolitan area, can reduce mortality and length of hospital stay

Bio-inspired Anomaly Detection for Low-cost Gas Sensors

This paper proposes a novel anomaly detection method for gas sensors using spiking neural network principles. The synapse models with excitatory/inhibitory responses and a single spiking neuron are employed to develop the bio-inspired anomaly detector for a single gas sensor. The approach can detect anomalies in the data, which is collected by the gas sensor by identifying rapid changes rather than a magnitude threshold. In particular, the false-positive detections due to the drifts of low-cost sensors are minimised using the proposed bio-inspired approach. Using the chemicals of surgical spirits and isobutanol as test substances, experiments were carried out to evaluate the proposed method. Results demonstrate that gas anomalies can be detected when the chemical substances are presented to the sensor. In addition, results show that the approach can detect under the presence of sensor drift. The proposed bio-inspired detector was implemented on FPGA hardware, which demonstrates relatively low resources. Compact and energy efficient CMOS-based implementations of the synapse are also available which supports the low-cost potential applications of this approach, e.g. use in safety with drones and ground robots in hazardous scene detection

A Novel Multi-objective Optimisation Algorithm for Routability and Timing Driven Circuit Clustering on FPGAs

Circuit clustering algorithms fit synthesised circuits into FPGA configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort required and performance achievable in subsequent place-and-route processes. Circuit clustering is limited by hardware constraints of specific target architectures. Hence, better circuit clustering approaches are essential for improving device utilisation whilst at the same time optimising circuit performance parameters such as, e.g., power and delay. In this paper, we present a method based on multi-objective genetic algorithm (MOGA) to facilitate circuit clustering. We address a number of challenges including CLB input bandwidth constraints, improvement of CLB utilisation, minimisation of interconnects between CLBs. Our new approach has been validated using the "Golden 20" MCNC benchmark circuits that are regularly used in FPGA-related literature. The results show that the method proposed in this paper achieves improvements of up to 50% in clustering, routability and timing when compared to state-of-the-art approaches including VPack, T-VPack, RPack, DPack, HDPack, MOPack and iRAC. Key contribution of this work is a flexible EDA flow that can incorporate numerous objectives required to successfully tackle real-world circuit design on FPGA, providing device utilisation at increased design performance

Fighting stochastic variability in a D-type flip-flop with transistor-level reconfiguration

In this study, the authors present a design optimisation case study of D-type flip-flop timing characteristics that are degraded as a result of intrinsic stochastic variability in a 25 nm technology process. What makes this work unique is that the design is mapped onto a multi-reconfigurable architecture, which is, like a field programmable gate array (FPGA), configurable at the gate level but can then be optimised using transistor level configuration options that are additionally built into the architecture. While a hardware VLSI prototype of this architecture is currently being fabricated, the results presented here are obtained from a virtual prototype implemented in SPICE using statistically enhanced 25 nm high performance metal gate MOSFET compact models from gold standard simulations for pre-fabrication verification. A D-type flip-flop is chosen as a benchmark in this study, and it is shown that timing characteristics that are degraded because of stochastic variability can be recovered and improved. This study highlights significant potential of the programmable analogue and digital array architecture to represent a next-generation FPGA architecture that can recover yield using post-fabrication transistor-level optimisation in addition to adjusting the operating point of mapped designs