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

High-Sigma Performance Analysis using Multi-Objective Evolutionary Algorithms

Semiconductor devices have rapidly improved in performance and function density over the past 25 years enabled by the continuous shrinking of technology feature sizes. Fabricating transistors that small, even with advanced processes, results in structural irregularities at the atomic scale, which affect device characteristics in a random manner. To simulate performance of circuits comprising a large number of devices using statistical models and ensuring low failure rates, performance outliers are required to be investigated. Standard Monte Carlo analysis will quickly become intractable because of the large number of circuit simulations required. Cases where the number of samples exceeds are known as “high-sigma problems”. This work proposes a highsigma sampling methodology based on multi-objective optimisation using evolutionary algorithms. A D-type Flip Flop is presented as a case study and it is shown that higher sigma outliers can be reached using a similar number of SPICE evaluations as Monte Carlo analysis

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

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

Implementation of Reduced Precision Integer Epigenetic Networks in Hardware

This paper details the development of a resource efficient implementation of the Artificial Epigenetic Network (AEN) concept, based on reduced precision integer mathematics, and the translation of this implementation into hardware via a Field Programmable Gate Array (FPGA) to provide improvements in resource utilisation and execution speed while not sacrificing the unique benefits provided by the epigenetic mechanism. Validation of the implementation’s performance on the inverted pendulum task is obtained and compared to that of previous AENs, as well as experiments to determine how far the precision of the network may be reduced while still maintaining an acceptable degree of performance

Local Fitness Landscape Exploration Based Genetic Algorithms

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm" (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems

Biogeochemistry: Early phosphorus redigested

Atmospheric oxygen was maintained at low levels throughout huge swathes of Earth's early history. Estimates of phosphorus availability through time suggest that scavenging from anoxic, iron-rich oceans stabilized this low-oxygen world

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

Lyophilisation of influenza, rabies and Marburg lentiviral pseudotype viruses for the development and distribution of a neutralisation-assay based diagnostic kit

Pseudotype viruses (PVs) are chimeric, replication-deficient virions that mimic wild-type virus entry mechanisms and can be safely employed in neutralisation assays, bypassing the need for high biosafety requirements and performing comparably to established serological assays. However, PV supernatant necessitates -80°C long-term storage and cold-chain maintenance during transport, which limits the scope of dissemination and application throughout resource-limited laboratories. We therefore investigated the effects of lyophilisation on influenza, rabies and Marburg PV stability, with a view to developing a pseudotype virus neutralisation assay (PVNA) based kit suitable for affordable global distribution. Infectivity of each PV was calculated after lyophilisation and immediate reconstitution, as well as subsequent to incubation of freeze-dried pellets at varying temperatures, humidities and timepoints. Integrity of glycoprotein structure following treatment was also assessed by employing lyophilised PVs in downstream PVNAs. In the presence of 0.5M sucrose-PBS cryoprotectant, each freeze-dried pseudotype was stably stored for 4 weeks at up to 37°C and could be neutralised to the same potency as unlyophilised PVs when employed in PVNAs. These results confirm the viability of a freeze-dried PVNA-based kit, which could significantly facilitate low-cost serology for a wide portfolio of emerging infectious viruses