How do we evaluate Artificial Immune Systems?

RAE2008

Learning Qualitative Metabolic Models

Coghill, G. M., Garrett, S. M. and King, R. D. (2004) Learning Qualitative Metabolic Models. European Conference on Artificial Intelligence (ECAI'04)

Structure based drug design with inductive logic programming

David P. Enot and Ross D. King (2003). Structure based drug design with inductive logic programming. The ACS National Meeting Spring 2003, New Orleans

The use of Inductive Logic Programming in drug design

David P. Enot and Ross D. King (2002) The use of Inductive Logic Programming in drug design. Proceedings of the 14th EuroQSAR Symposium (EuroQSAR 2002). Blackwell Publishing, p247-250

Learning Qualitative Models in the Presence of Noise

G. M. Coghill, S. M. Garrett and R. D. King (2002), Learning Qualitative Models in the Presence of Noise, QR'02 Workshop on Qualitative Reasoning

Vegetation and Topographic Control of Wind-blown Snow Distributions in Distributed and Aggregated Simulations for an Arctic Tundra Basin

Essery, RLH & JW, Pomeroy, (2004). Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations. Journal of Hydrometeorology, 5, 735-744.

GC-MS Peak Labeling Under ArMet

Jenkins, H., Beckmann, M., Draper, J., & Hardy, N. (2007). GC-MS Peak Labeling Under ArMet. Nikolau, B. J., & Wurtele, E. S. (Eds.), In: Concepts in Plant Metabolomics. (pp. 19-28). Dordrecht: Springer. Sponsorship: The authors gratefully acknowledge the United Kingdom Food Standards Agency (under the G02006 project) for support of their work in metabolomics.

Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches

Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selections described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.

OPEN DOORS AND OPEN MINDS: WHAT FACULTY AUTHORS CAN DO TO ENSURE OPEN ACCESS TO THEIR WORK THROUGH THEIR INSTITUTION

The Internet has brought unparalleled opportunities for expanding availability of research by bringing down economic and physical barriers to sharing. The digitally networked environment promises to democratize access, carry knowledge beyond traditional research niches, accelerate discovery, encourage new and interdisciplinary approaches to ever more complex research challenges, and enable new computational research strategies. However, despite these opportunities for increasing access to knowledge, the prices of scholarly journals have risen sharply over the past two decades, often forcing libraries to cancel subscriptions. Today even the wealthiest institutions cannot afford to sustain all of the journals needed by their faculties and students. To take advantage of the opportunities created by the Internet and to further their mission of creating, preserving, and disseminating knowledge, many academic institutions are taking steps to capture the benefits of more open research sharing. Colleges and universities have built digital repositories to preserve and distribute faculty scholarly articles and other research outputs. Many individual authors have taken steps to retain the rights they need, under copyright law, to allow their work to be made freely available on the Internet and in their institutionâ s repository. And, faculties at some institutions have adopted resolutions endorsing more open access to scholarly articles. Most recently, on February 12, 2008, the Faculty of Arts and Sciences (FAS) at Harvard University took a landmark step. The faculty voted to adopt a policy requiring that faculty authors send an electronic copy of their scholarly articles to the universityâ s digital repository and that faculty authors automatically grant copyright permission to the university to archive and to distribute these articles unless a faculty member has waived the policy for a particular article. Essentially, the faculty voted to make open access to the results of their published journal articles the default policy for the Faculty of Arts and Sciences of Harvard University. As of March 2008, a proposal is also under consideration in the University of California system by which faculty authors would commit routinely to grant copyright permission to the university to make copies of the facultyâ s scholarly work openly accessible over the Internet. Inspired by the example set by the Harvard faculty, this White Paper is addressed to the faculty and administrators of academic institutions who support equitable access to scholarly research and knowledge, and who believe that the institution can play an important role as steward of the scholarly literature produced by its faculty. This paper discusses both the motivation and the process for establishing a binding institutional policy that automatically grants a copyright license from each faculty member to permit deposit of his or her peer-reviewed scholarly articles in institutional repositories, from which the works become available for others to read and cite.

A Short History of Computational Complexity

National Science Foundation (CCR-998310); Army Research Office (DAAD19-02-1-0058)

Computational Properties of SNAFU

Sensor applications in Sensoria [1] are expressed using STEP (Sensorium Task Execution Plan). SNAFU (Sensor-Net Applications as Functional Units) serves as a high-level sensor-programming language, which is compiled into STEP. In SNAFU’s current form, its differences with STEP are relatively minor, as they are limited to shorthands and macros not available in STEP. We show that, however restrictive it may seem, SNAFU has in fact universal power; technically, it is a Turing-complete language, i.e., any Turing program can be written in SNAFU (though not always conveniently). Although STEP may be allowed to have universal power, as a low-level language not directly available to Sensorium users, SNAFU programmers may use this power for malicious purposes or inadvertently introduce errors with destructive consequences. In future developments of SNAFU, we plan to introduce restrictions and highlevel features with safety guards, such as those provided by a type system, which will make SNAFU programming safer.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

Computational studies of the transverse structure of AGN jets

Both the emission properties and the evolution of the radio jets of Active Galactic Nuclei are dependent on the magnetic (B) fields that thread them. A number of observations of AGN jets suggest that the B fields they carry have a significant helical component, at least on parsec scales. This thesis uses a model, first proposed by Laing and then developed by Papageorgiou, to explore how well the observed properties of AGN jets can be reproduced by assuming a helical B field with three parameters; pitch angle, viewing angle and degree of entanglement. This model has been applied to multifrequency Very Long Baseline Interferometry (VLBI) observations of the AGN jets of Markarian 501 and M87, making it possible to derive values for the helical pitch angle, the viewing angle and the degree of entanglement for these jets. Faraday rotation measurements are another important tool for investigating the B fields of AGN jets. A helical B field component should result in a systematic gradient in the observed Faraday rotation across the jet. Real observed radio images have finite resolution; typical beam sizes for cm-wavelength VLBI observations are often comparable to or larger than the intrinsic jet widths, raising questions about how well resolved a jet must be in the transverse direction in order to reliably detect transverse Faraday-rotation structure. This thesis presents results of Monte Carlo simulations of Faraday rotation images designed to directly investigate this question, together with a detailed investigation into the probabilities of observing spurious Faraday Rotation gradients as a result of random noise and finite resolution. These simulations clearly demonstrate the possibility of detecting transverse Faraday-rotation structures even when the intrinsic jet widths are appreciably smaller than the beam width.

Electrochemical biosensor based on microfabricated electrode arrays for life sciences applications

In developing a biosensor, the utmost important aspects that need to be emphasized are the specificity and selectivity of the transducer. These two vital prerequisites are of paramount in ensuring a robust and reliable biosensor. Improvements in electrochemical sensors can be achieved by using microelectrodes and to modify the electrode surface (using chemical or biological recognition layers to improve the sensitivity and selectivity). The fabrication and characterisations of silicon-based and glass-based gold microelectrode arrays with various geometries (band and disc) and dimension (ranging from 10 μm-100 nm) were reported. It was found that silicon-based transducers of 10 μm gold microelectrode array exhibited the most stable and reproducible electrochemical measurements hence this dimension was selected for further study. Chemical electrodeposition on both 10 μm microband and microdisc were found viable by electro-assisted self-assembled sol-gel silica film and nanoporous-gold electrodeposition respectively. The fabrication and characterisations of on-chip electrochemical cell was also reported with a fixed diameter/width dimension and interspacing variation. With this regard, the 10 μm microelectrode array with interspacing distance of 100 μm exhibited the best electrochemical response. Surface functionalisations on single chip of planar gold macroelectrodes were also studied for the immobilisation of histidine-tagged protein and antibody. Imaging techniques such as atomic force microscopy, fluorescent microscopy or scanning electron microscope were employed to complement the electrochemical characterisations. The long-chain thiol of self-assembled monolayer with NTA-metal ligand coordination was selected for the histidine-tagged protein while silanisation technique was selected for the antibody immobilisation. The final part of the thesis described the development of a T-2 labelless immunosensor using impedimetric approach. Good antibody calibration curve was obtained for both 10 μm microband and 10 μm microdisc array. For the establishment of the T-2/HT-2 toxin calibration curve, it was found that larger microdisc array dimension was required to produce better calibration curve. The calibration curves established in buffer solution show that the microelectrode arrays were sensitive and able to detect levels of T-2/HT-2 toxin as low as 25 ppb (25 μg kg-1) with a limit of quantitation of 4.89 ppb for a 10 μm microband array and 1.53 ppb for the 40 μm microdisc array.