Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques.

Systematic identification of small molecule adjuvants

Introduction: Adjuvants potentiate immune responses, reducing the amount and dosing frequency of antigen required for inducing protective immunity. Adjuvants are of special importance when considering subunit, epitope-based or more unusual vaccine formulations lacking significant innate immunogenicity. While numerous adjuvants are known, only a few are licensed for human use; principally alum, and squalene-based oil-in-water adjuvants. Alum, the most commonly used, is suboptimal. There are many varieties of adjuvant: proteins, oligonucleotides, drug-like small molecules and liposome-based delivery systems with intrinsic adjuvant activity being perhaps the most prominent. Areas covered: This article focuses on small molecules acting as adjuvants, with the author reviewing their current status while highlighting their potential for systematic discovery and rational optimisation. Known small molecule adjuvants (SMAs) can be synthetically complex natural products, small oligonucleotides or drug-like synthetic molecules. The author provides examples of each class, discussing adjuvant mechanisms relevant to SMAs, and exploring the high-throughput discovery of SMAs. Expert opinion: SMAs, particularly synthetic drug-like adjuvants, are amenable to the plethora of drug-discovery techniques able to optimise the properties of biologically active small molecules. These range from laborious synthetic modifications to modern, rational, effort-efficient computational approaches, such as QSAR and structure-based drug design. In principal, any property or characteristic can thus be designed in or out of compounds, allowing us to tailor SMAs to specific biological functions, such as targeting specific cells or pathways, in turn affording the power to tailor SMAs to better address different diseases.

The slope of the psychometric function and non-stationarity of thresholds in spatiotemporal contrast vision

The slope of the two-interval, forced-choice psychometric function (e.g. the Weibull parameter, ß) provides valuable information about the relationship between contrast sensitivity and signal strength. However, little is known about how or whether ß varies with stimulus parameters such as spatiotemporal frequency and stimulus size and shape. A second unresolved issue concerns the best way to estimate the slope of the psychometric function. For example, if an observer is non-stationary (e.g. their threshold drifts between experimental sessions), ß will be underestimated if curve fitting is performed after collapsing the data across experimental sessions. We measured psychometric functions for 2 experienced observers for 14 different spatiotemporal configurations of pulsed or flickering grating patches and bars on each of 8 days. We found ß ˜ 3 to be fairly constant across almost all conditions, consistent with a fixed nonlinear contrast transducer and/or a constant level of intrinsic stimulus uncertainty (e.g. a square law transducer and a low level of intrinsic uncertainty). Our analysis showed that estimating a single ß from results averaged over several experimental sessions was slightly more accurate than averaging multiple estimates from several experimental sessions. However, the small levels of non-stationarity (SD ˜ 0.8 dB) meant that the difference between the estimates was, in practice, negligible.

Paradoxical psychometric functions ("swan functions") are explained by dilution masking in four stimulus dimensions

The visual system dissects the retinal image into millions of local analyses along numerous visual dimensions. However, our perceptions of the world are not fragmentary, so further processes must be involved in stitching it all back together. Simply summing up the responses would not work because this would convey an increase in image contrast with an increase in the number of mechanisms stimulated. Here, we consider a generic model of signal combination and counter-suppression designed to address this problem. The model is derived and tested for simple stimulus pairings (e.g. A + B), but is readily extended over multiple analysers. The model can account for nonlinear contrast transduction, dilution masking, and signal combination at threshold and above. It also predicts nonmonotonic psychometric functions where sensitivity to signal A in the presence of pedestal B first declines with increasing signal strength (paradoxically dropping below 50% correct in two-interval forced choice), but then rises back up again, producing a contour that follows the wings and neck of a swan. We looked for and found these "swan" functions in four different stimulus dimensions (ocularity, space, orientation, and time), providing some support for our proposal.

Signal processing for molecular and cellular biological physics:an emerging field

Recent advances in our ability to watch the molecular and cellular processes of life in action-such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer-raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

Nurturing communities of practice for effective university-to-industry technology transfer:two exemplar cases from the Cauca Region of Columbia

This paper’s primary aim is to demonstrate how university-industry technology transfer can be achieved effectively by nurturing and bridging communities of practice amongst recipients of technology and stakeholders concerned with technology diffusion, productivity and economic development. Its empirical evidence is from an intervention initiative targeting two small-scale industries, namely fish farming and coffee production, in the Cauca region of Colombia. Results show how barriers to transfer have been overcome and the intervention’s design elements and outcomes are discussed.

Policy transfer in practice:implementing supplier diversity in the UK

This article draws on the policy transfer literature to examine a UK-based initiative to promote supplier diversity and provides insights into three areas neglected in current research, namely: the dynamics of non-governmental policy transfer; the factors that mediate policy transfer in different jurisdictions; and the integration of research and practice in small business related policy transfer. To this end, an innovative action research approach is deployed with the dual purpose of effecting practical change and advancing knowledge. This comprises two phases: first, a realist analysis of the US National Minority Supplier Development Council (NMSDC), an exemplar intermediary; and second, the implementation and concurrent realist evaluation of a supplier diversity initiative modelled on NMSDC, referred to as ‘Supplier Development East Midlands’ (SDEM). The findings provide lessons for academics and practitioners dealing with small and medium-sized enterprise (SME) policy transfer in general and supplier diversity intermediaries in particular.

The need for wider supply chain management adoption:empirical results from Ireland

Purpose - This paper aims to provide empirical results which suggest that there is a need for more widespread adoption of supply chain management among Irish firms. Design/methodology/approach - The Republic of Ireland is a small, open, trade-dependent economy and is one of the fastest growing economies in the developed world. However, due to rising costs, there is an increasing trend in Ireland to outsource lower function manufacturing processes to lower-cost locations but to retain high-skill functions (such as R&D). This trend, together with other factors such as its peripheral location, suggests that supply chain management is critical from an Irish perspective. In order to gain unique insights of current levels of awareness/adoption of SCM and the potential impact SCM could have on competitiveness, a survey was conducted among 776 Irish firms. Findings - Overall, the findings suggest that many firms in Ireland pay lip-service to the importance of SCM elements and objectives but the majority of firms, about two thirds, have only a passing understanding of what constitutes SCM. Only 25 per cent adopt SCM programmes and only 9 per cent of Irish companies have a specialised SCM or logistics manager. The gaps in their understanding of SCM are matched by the gaps in their awareness of key costs (e.g. 59 per cent of companies do not know their total supply chain costs). While there are supply chain management adopters in Ireland that are already well up the s-curve of innovation transfer, it is the larger group of less aware companies that must become better at how they manage their supply chains. Originality/value - The paper offers a useful insight into supply chain management and its role in Irish industry. © Emerald Group Publishing Limited.

LIPPRED:a web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies. © 2003 Elsevier B.V. All rights reserved.

Knowledge transfer in website design:exploring the processes and benefits of design collaboration for non-creative Micros

This thesis explores the interaction between Micros (<10 employees) from non-creative sectors and website designers ("Creatives") that occurred when creating a website of a higher order than a basic template site. The research used Straussian Grounded Theory Method with a longitudinal design, in order to identify what knowledge transferred to the Micros during the collaboration, how it transferred, what factors affected the transfer and outcomes of the transfer including behavioural additionality. To identify whether the research could be extended beyond this, five other design areas were also examined, as well as five Small to Medium Enterprises (SMEs) engaged in website and branding projects. The findings were that, at the start of the design process, many Micros could not articulate their customer knowledge, and had poor marketing and visual language skills, knowledge core to web design, enabling targeted communication to customers through images. Despite these gaps, most Micros still tried to lead the process. To overcome this disjoint, the majority of the designers used a knowledge transfer strategy termed in this thesis as ‘Bi-Modal Knowledge Transfer’, where the Creative was aware of the transfer but the Micro was unaware, both for drawing out customer knowledge from the Micro and for transferring visual language skills to the Micro. Two models were developed to represent this process. Two models were also created to map changes in the knowledge landscapes of customer knowledge and visual language – the Knowledge Placement Model and the Visual Language Scale. The Knowledge Placement model was used to map the placement of customer knowledge within the consciousness, extending the known Automatic-Unconscious -Conscious model, adding two more locations – Peripheral Consciousness and Occasional Consciousness. Peripheral Consciousness is where potential knowledge is held, but not used. Occasional Consciousness is where potential knowledge is held but used only for specific tasks. The Visual Language Scale was created to measure visual language ability from visually responsive, where the participant only responds personally to visual symbols, to visually multi-lingual, where the participant can use visual symbols to communicate with multiple thought-worlds. With successful Bi-Modal Knowledge Transfer, the outcome included not only an effective website but also changes in the knowledge landscape for the Micros and ongoing behavioural changes, especially in marketing. These effects were not seen in the other design projects, and only in two of the SME projects. The key factors for this difference between SMEs and Micros appeared to be an expectation of knowledge by the Creatives and failure by the SMEs to transfer knowledge within the company.

Bridging large and small scales of water models using hybrid Molecular Dynamics/Fluctuating Hydrodynamics framework

This thesis presents a two-dimensional water model investigation and development of a multiscale method for the modelling of large systems, such as virus in water or peptide immersed in the solvent. We have implemented a two-dimensional ‘Mercedes Benz’ (MB) or BN2D water model using Molecular Dynamics. We have studied its dynamical and structural properties dependence on the model’s parameters. For the first time we derived formulas to calculate thermodynamic properties of the MB model in the microcanonical (NVE) ensemble. We also derived equations of motion in the isothermal–isobaric (NPT) ensemble. We have analysed the rotational degree of freedom of the model in both ensembles. We have developed and implemented a self-consistent multiscale method, which is able to communicate micro- and macro- scales. This multiscale method assumes, that matter consists of the two phases. One phase is related to micro- and the other to macroscale. We simulate the macro scale using Landau Lifshitz-Fluctuating Hydrodynamics, while we describe the microscale using Molecular Dynamics. We have demonstrated that the communication between the disparate scales is possible without introduction of fictitious interface or approximations which reduce the accuracy of the information exchange between the scales. We have investigated control parameters, which were introduced to control the contribution of each phases to the matter behaviour. We have shown, that microscales inherit dynamical properties of the macroscales and vice versa, depending on the concentration of each phase. We have shown, that Radial Distribution Function is not altered and velocity autocorrelation functions are gradually transformed, from Molecular Dynamics to Fluctuating Hydrodynamics description, when phase balance is changed. In this work we test our multiscale method for the liquid argon, BN2D and SPC/E water models. For the SPC/E water model we investigate microscale fluctuations which are computed using advanced mapping technique of the small scales to the large scales, which was developed by Voulgarakisand et. al.

Enhanced information transmission with signal-dependent noise in an array of nonlinear elements

We have investigated information transmission in an array of threshold units that have signal-dependent noise and a common input signal. We demonstrate a phenomenon similar to stochastic resonance and suprathreshold stochastic resonance with additive noise and show that information transmission can be enhanced by a nonzero level of noise. By comparing system performance to one with additive noise we also demonstrate that the information transmission of weak signals is significantly better with signal-dependent noise. Indeed, information rates are not compromised even for arbitrary small input signals. Furthermore, by an appropriate selection of parameters, we observe that the information can be made to be (almost) independent of the level of the noise, thus providing a robust method of transmitting information in the presence of noise. These result could imply that the ability of hair cells to code and transmit sensory information in biological sensory systems is not limited by the level of signal-dependent noise. © 2007 The American Physical Society.

Differential impact of amino acid substitutions on critical residues of the human glucagon-like peptide-1 receptor involved in peptide activity and small-molecule allosterys

The glucagon-like peptide-1 receptor (GLP-1R) is a class B G protein-coupled receptor that has a critical role in the regulation of glucose homeostasis, principally through the regulation of insulin secretion. The receptor systemis highly complex, able to be activated by both endogenous [GLP-1(1-36)NH2, GLP-1(1-37), GLP-1(7-36)NH2, GLP-1(7-37), oxyntomodulin], and exogenous (exendin-4) peptides in addition to small-molecule allosteric agonists (compound 2 [6,7-dichloro-2-methylsulfonyl-3-tertbutylaminoquinoxaline], BETP [4-(3-benzyloxy)phenyl)-2-ethylsulfinyl-6-(trifluoromethyl)pyrimidine]). Furthermore, the GLP-1R is subject to single-nucleotide polymorphic variance, resulting in amino acid changes in the receptor protein. In this study, we investigated two polymorphic variants previously reported to impact peptidemediated receptor activity (M149) and small-molecule allostery (C333). These residues were mutated to a series of alternate amino acids, and their functionality was monitored across physiologically significant signaling pathways, including cAMP, extracellular signal-regulated kinase 1 and 2 phosphorylation, and intracellular Ca2+ mobilization, in addition to peptide binding and cell-surface expression. We observed that residue 149 is highly sensitive to mutation, with almost all peptide responses significantly attenuated at mutated receptors. However, most reductions in activity were able to be restored by the small-molecule allosteric agonist compound 2. Conversely, mutation of residue 333 had little impact on peptide-mediated receptor activation, but this activity could not be modulated by compound 2 to the same extent as that observed at the wild-type receptor. These results provide insight into the importance of residues 149 and 333 in peptide function and highlight the complexities of allosteric modulation within this receptor system.

Extreme Situations Prediction by MultidimenSional Heterogeneous Time Series Using Logical Decision Functions

* The work is supported by RFBR, grant 04-01-00858-a