Assessment of different formulations of oral Mycobacterium bovis Bacille Calmette-Guerin (BCG) vaccine in rodent models for immunogenicity and protection against aerosol challenge with M-bovis

Bovine tuberculosis (bTB) caused by infection with Mycobacterium bovis is causing considerable economic loss to farmers and Government in the United Kingdom as its incidence is increasing. Efforts to control bTB in the UK are hampered by the infection in Eurasian badgers (Metes metes) that represent a wildlife reservoir and source of recurrent M. bovis exposure to cattle. Vaccination of badgers with the human TB vaccine, M. bovis Bacille Calmette-Guerin (BCG), in oral bait represents a possible disease control tool and holds the best prospect for reaching badger populations over a wide geographical area. Using mouse and guinea pig models, we evaluated the immunogenicity and protective efficacy, respectively, of candidate badger oral vaccines based on formulation of BCG in lipid matrix, alginate beads, or a novel microcapsular hybrid of both lipid and alginate. Two different oral doses of BCG were evaluated in each formulation for their protective efficacy in guinea pigs, while a single dose was evaluated in mice. In mice, significant immune responses (based on lymphocyte proliferation and expression of IFN-gamma) were only seen with the lipid matrix and the lipid in alginate microcapsular formulation, corresponding to the isolation of viable BCG from alimentary tract lymph nodes. In guinea pigs, only BCG formulated in lipid matrix conferred protection to the spleen and lungs following aerosol route challenge with M. bovis. Protection was seen with delivery doses in the range 10(6)-10(7) CFU, although this was more consistent in the spleen at the higher dose. No protection in terms of organ CFU was seen with BCG administered in alginate beads or in lipid in alginate microcapsules, although 10(7) in the latter formulation conferred protection in terms of increasing body weight after challenge and a smaller lung to body weight ratio at necropsy. These results highlight the potential for lipid, rather than alginate, -based vaccine formulations as suitable delivery vehicles for an oral BCG vaccine in badgers.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

A survey of economic growth

Understanding the process of economic growth has been called the ultimate objective of economics. It has also been likened to an elusive quest – like the Holy Grail or the Elixir of Life (Easterly 2001). Taking on such a quest requires ingenuity and perseverance. Even small insights along the way can have major benefits to millions of people; small mistakes can do the reverse. Economies which achieve large increases in output over extended periods of time, not only enable rapid increases in standards of living, but also have dramatic changes in the economic, political and social landscape. For example, the USA is estimated to produce approximately 30 times as much in 1999 as it did in 1899. This sustained economic growth means that in 1999 the USA had an average income per capita of US$34 100. In contrast, sub-Saharan Africa had an average income of $490. Understanding these vast income differences, produced over many decades, is the elusive quest. The aim of this survey is to explain how economists try to understand the process of economic growth. To make the task manageable, the focus is on major issues and current debates. Models and conceptual frameworks are discussed in section III. Section IV summarises empirical studies, with a particular focus on econometric studies of groups of countries. This is not to say that case studies of single countries are not valuable, but space precludes covering everything. The following section sets out some facts about economic growth and, hopefully, motivates the further effort needed to tackle the theory and econometrics.

Typical performance of regular low-density parity-check codes over general symmetric channels

Typical performance of low-density parity-check (LDPC) codes over a general binary-input output-symmetric memoryless channel is investigated using methods of statistical mechanics. Relationship between the free energy in statistical-mechanics approach and the mutual information used in the information-theory literature is established within a general framework; Gallager and MacKay-Neal codes are studied as specific examples of LDPC codes. It is shown that basic properties of these codes known for particular channels, including their potential to saturate Shannon's bound, hold for general symmetric channels. The binary-input additive-white-Gaussian-noise channel and the binary-input Laplace channel are considered as specific channel models.

Using the max-sum algorithm for supply chain emergence in dynamic multiunit environments

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.

Verification and validation of numerical models of the transport of insulation debris

A combination of the two-fluid and drift flux models have been used to model the transport of fibrous debris. This debris is generated during loss of coolant accidents in the primary circuit of pressurized or boiling water nuclear reactors, as high pressure steam or water jets can damage adjacent insulation materials including mineral wool blankets. Fibre agglomerates released from the mineral wools may reach the containment sump strainers, where they can accumulate and compromise the long-term operation of the emergency core cooling system. Single-effect experiments of sedimentation in a quiescent rectangular column and sedimentation in a horizontal flow are used to verify and validate this particular application of the multiphase numerical models. The utilization of both modeling approaches allows a number of pseudocontinuous dispersed phases of spherical wetted agglomerates to be modeled simultaneously. Key effects on the transport of the fibre agglomerates are particle size, density and turbulent dispersion, as well as the relative viscosity of the fluid-fibre mixture.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanismbehind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

An OTDR and gratings assisted multifunctional fiber sensing system

We report a distributed multifunctional fiber sensing network based on weak-fiber Bragg gratings (WFBGs) and long period fiber grating (LPG) assisted OTDR system. The WFBGs are applied for temperature, strain, and vibration monitoring at key position, and the LPG is used as a linear filter in the system to convert the wavelength shift of WFBGs caused by environmental change into the power change. The simulation results show that it is possible to integrate more than 4472 WFBGs in the system when the reflectivity of WFBGs is less than {10}^{-5}. Besides, the back-Rayleigh scattering along the whole fiber can also be detected which makes distributed bend sensing possible. As an experimental demonstration, we have used three WFBGs UV-inscribed with 50-m interval at the end of a 2.6-km long fiber, which part was subjected for temperature, strain, and vibration sensing, respectively. The ratio of the intensity of output and input light is used for temperature and strain sensing, and the results show strain and temperature sensitivities are 4.2 \times {10}^{-4}{/\mu \varepsilon } and 5.9 \times {10}^{-3}{{/ {^{\circ }}\textrm {C}}} , respectively. Detection of multiple vibrations and single vibration with the broad frequency band up to 500 Hz are also achieved. In addition, distributed bend sensing which could be simultaneously realized in this system has been proposed.

Recent advances in explaining hedge fund returns:implicit factors and exposures

We survey articles covering how hedge fund returns are explained, using largely non-linear multifactor models that examine the non-linear pay-offs and exposures of hedge funds. We provide an integrated view of the implicit factor and statistical factor models that are largely able to explain the hedge fund return-generating process. We present their evolution through time by discussing pioneering studies that made a significant contribution to knowledge, and also recent innovative studies that examine hedge fund exposures using advanced econometric methods. This is the first review that analyzes very recent studies that explain a large part of hedge fund variation. We conclude by presenting some gaps for future research.