A Bayesian approach to tracking in single molecule fluorescence microscopy

Using fluorescence microscopy with single molecule sensitivity it is now possible to follow the movement of individual fluorophore tagged molecules such as proteins and lipids in the cell membrane with nanometer precision. These experiments are important as they allow many key biological processes on the cell membrane and in the cell, such as transcription, translation and DNA replication, to be studied at new levels of detail. Computerized microscopes generate sequences of images (in the order of tens to hundreds) of the molecules diffusing and one of the challenges is to track these molecules to obtain reliable statistics such as speed distributions, diffusion patterns, intracellular positioning, etc. The data set is challenging because the molecules are tagged with a single or small number of fluorophores, which makes it difficult to distinguish them from the background, the fluorophore bleaches irreversibly over time, the number of tagged molecules are unknown and there is occasional loss of signal from the tagged molecules. All these factors make accurate tracking over long trajectories difficult. Also the experiments are technically difficulty to conduct and thus there is a pressing need to develop better algorithms to extract the maximum information from the data. For this purpose we propose a Bayesian approach and apply our technique to synthetic and a real experimental data set.

Bayesian and L1 Approaches to Sparse Unsupervised Learning

The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance.

Integrated billing mechanisms in the Internet of Things to support information sharing and enable new business opportunities

The Internet of Things (IOT) concept and enabling technologies such as RFID offer the prospect of linking the real world of physical objects with the virtual world of information technology to improve visibility and traceability information within supply chains and across the entire lifecycles of products, as well as enabling more intuitive interactions and greater automation possibilities. There is a huge potential for savings through process optimization and profit generation within the IOT, but the sharing of financial benefits across companies remains an unsolved issue. Existing approaches towards sharing of costs and benefits have failed to scale so far. The integration of payment solutions into the IOT architecture could solve this problem. We have reviewed different possible levels of integration. Multiple payment solutions have been researched. Finally we have developed a model that meets the requirements of the IOT in relation to openness and scalability. It supports both hardware-centric and software-centric approaches to integration of payment solutions with the IOT. Different requirements concerning payment solutions within the IOT have been defined and considered in the proposed model. Possible solution providers include telcos, e-payment service providers and new players such as banks and standardization bodies. The proposed model of integrating the Internet of Things with payment solutions will lower the barrier to invoicing for the more granular visibility information generated using the IOT. Thus, it has the potential to enable recovery of the necessary investments in IOT infrastructure and accelerate adoption of the IOT, especially for projects that are only viable when multiple benefits throughout the supply chain need to be accumulated in order to achieve a Return on Investment (ROI). In a long-term perspective, it may enable IT-departments to become profit centres instead of cost centres. © 2010 - IOS Press and the authors. All rights reserved.

Managing water resources and their connection to land and energy

Effective management is a key to ensuring the current and future sustainability of land, water and energy resources. Identifying the complexities of such management is not an easy task, especially since past studies have focussed on studying these resources in isolation from one another. However, with rapid population growth and an increase in the awareness of a potential change in climatic conditions that may affect the demand for and supply of food, water and energy, there has been a growing need to integrate the planning decisions relating to these three resources. The paper shows the visualisation of linked resources by drawing a set of interconnected Sankey diagrams for energy, water and land. These track the changes from basic resource (e.g. coal, surface water, groundwater and cropland) through transformations (e.g. fuel refining and desalination) to final services (e.g. sustenance, hygiene and transportation). The focus here is on the water analysis aspects of the tool, which uses California as a detailed case study. The movement of water in California is traced from its source to its services by mapping the different transformations of water from when it becomes available, through its use, to further treatment, to final sinks (including recycling and reuse of that resource). The connections that water has with energy and land resources for the state of California are highlighted. This includes the amount of energy used to pump and treat water, and the amount of water used for energy production and the land resources which create a water demand to produce crops for food. By mapping water in this way, policy-makers and resource managers can more easily understand the competing uses of water (environment, agriculture and urban use) through the identification of the services it delivers (e.g. sanitation, agriculture, landscaping), the potential opportunities for improving the management of the resource (e.g. building new desalination plants, reducing the demand for services), and the connections with other resources which are often overlooked in a traditional sector-based management strategy.

A Computer integrated unified modelling approach to responsive manufacturing

Computer modelling approaches have significant potential to enable decision-making about various aspects of responsive manufacturing. In order to understand the system prior to the selection of any responsiveness strategy, multiple process segments of organisations need to be modelled. The article presents a novel systematic approach for creating coherent sets of unified enterprise, simulation and other supporting models that collectively facilitate responsiveness. In this approach, enterprise models are used to explicitly define relatively enduring relationships between (i) production planning and control (PPC) processes, that implement a particular strategy and (ii) process-oriented elements of production systems, that are work loaded by the PPC processes. Coherent simulation models, can in part be derived from the enterprise models, so that they computer execute production system behaviours. In this way, time-based performance outcomes can be simulated; so that the impacts of alternative PPC strategies on the planning and controlling historical or forecasted patterns of workflow, through (current and possible future) production system models, can be analysed. The article describes the unified modelling approach conceived and its application in a furniture industry case study small and medium enterprise (SME). Copyright © 2010 Inderscience Enterprises Ltd.

Thylakoid terminal oxidases are essential for the cyanobacterium Synechocystis sp. PCC 6803 to survive rapidly changing light intensities.

Cyanobacteria perform photosynthesis and respiration in the thylakoid membrane, suggesting that the two processes are interlinked. However, the role of the respiratory electron transfer chain under natural environmental conditions has not been established. Through targeted gene disruption, mutants of Synechocystis sp. PCC 6803 were generated that lacked combinations of the three terminal oxidases: the thylakoid membrane-localized cytochrome c oxidase (COX) and quinol oxidase (Cyd) and the cytoplasmic membrane-localized alternative respiratory terminal oxidase. All strains demonstrated similar growth under continuous moderate or high light or 12-h moderate-light/dark square-wave cycles. However, under 12-h high-light/dark square-wave cycles, the COX/Cyd mutant displayed impaired growth and was completely photobleached after approximately 2 d. In contrast, use of sinusoidal light/dark cycles to simulate natural diurnal conditions resulted in little photobleaching, although growth was slower. Under high-light/dark square-wave cycles, the COX/Cyd mutant suffered a significant loss of photosynthetic efficiency during dark periods, a greater level of oxidative stress, and reduced glycogen degradation compared with the wild type. The mutant was susceptible to photoinhibition under pulsing but not constant light. These findings confirm a role for thylakoid-localized terminal oxidases in efficient dark respiration, reduction of oxidative stress, and accommodation of sudden light changes, demonstrating the strong selective pressure to maintain linked photosynthetic and respiratory electron chains within the thylakoid membrane. To our knowledge, this study is the first to report a phenotypic difference in growth between terminal oxidase mutants and wild-type cells and highlights the need to examine mutant phenotypes under a range of conditions.

Transient crosstalk in LCOS based WSS and a method to suppress the crosstalk levels

The origin of the transient crosstalk (TC) in a phase-only LCOS based WSS using a Fourier transform setup was investigated and identified. Two methods were proposed to reduce the TC by at least 5dB without the need to modify the optics or electronics in use. © 2013 OSA.

Enhanced integrated modelling approach to reconfiguring manufacturing enterprises

Dynamism and uncertainty are real challenges for present day manufacturing enterprises (MEs). Reasons include: an increasing demand for customisation, reduced time to market, shortened product life cycles and globalisation. MEs can reduce competitive pressure by becoming reconfigurable and change-capable. However, modern manufacturing philosophies, including agile and lean, must complement the application of reconfigurable manufacturing paradigms. Choosing and applying the best philosophies and techniques is very difficult as most MEs deploy complex and unique configurations of processes and resource systems, and seek economies of scope and scale in respect of changing and distinctive product flows. It follows that systematic methods of achieving model driven reconfiguration and interoperation of component based manufacturing systems are required to design, engineer and change future MEs. This thesis, titled Enhanced Integrated Modelling Approach to Reconfiguring Manufacturing Enterprises , introduces the development and prototyping a model-driven environment for the design, engineering, optimisation and control of the reconfiguration of MEs with an embedded capability to handle various types of change. The thesis describes a novel systematic approach, namely enhanced integrated modelling approach (EIMA), in which coherent sets of integrated models are created that facilitates the engineering of MEs especially their production planning and control (PPC) systems. The developed environment supports the engineering of common types of strategic, tactical and operational processes found in many MEs. The EIMA is centred on the ISO standardised CIMOSA process modelling approach. Early study led to the development of simulation models during which various CIMOSA shortcomings were observed, especially in its support for aspects of ME dynamism. A need was raised to structure and create semantically enriched models hence forming an enhanced integrated modelling environment. The thesis also presents three industrial case examples: (1) Ford Motor Company; (2) Bradgate Furniture Manufacturing Company; and (3) ACM Bearings Company. In order to understand the system prior to realisation of any PPC strategy, multiple process segments of any target organisation need to be modelled. Coherent multi-perspective case study models are presented that have facilitated process reengineering and associated resource system configuration. Such models have a capability to enable PPC decision making processes in support of the reconfiguration of MEs. During these case studies, capabilities of a number of software tools were exploited such as Arena®, Simul8®, Plant Simulation®, MS Visio®, and MS Excel®. Case study results demonstrated effectiveness of the concepts related to the EIMA. The research has resulted in new contributions to knowledge in terms of new understandings, concepts and methods in following ways: (1) a structured model driven integrated approach to the design, optimisation and control of future reconfiguration of MEs. The EIMA is an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of an ME; and (2) example application cases showing benefits in terms of reduction in lead time, cost and resource load and in terms of improved responsiveness of processes and resource systems with a special focus on PPC; (3) identification and industrial application of a new key performance indicator (KPI) known as P3C the measuring and monitoring of which can aid in enhancing reconfigurability and responsiveness of MEs; and (4) an enriched modelling concept framework (E-MUNE) to capture requirements of static and dynamic aspects of MEs where the conceptual framework has the capability to be extended and modified according to the requirements. The thesis outlines key areas outlining a need for future research into integrated modelling approaches, interoperation and updating mechanisms of partial models in support of the reconfiguration of MEs.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Transient Crosstalk in LCOS Based WSS and a Method to Suppress the Crosstalk Levels

The origin of the transient crosstalk (TC) in a phase-only LCOS based WSS using a Fourier transform setup was investigated and identified. Two methods were proposed to reduce the TC by at least 5dB without the need to modify the optics or electronics in use. © 2013 OSA.