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Modular programming for tuberculosis control, the "AuTuMN" platform.

ABSTRACT: Tuberculosis (TB) is now the world's leading infectious killer and major programmatic advances will be needed if we are to meet the ambitious new End TB Targets. Although mathematical models are powerful tools for TB control, such models must be flexible enough to capture the complexity and heterogeneity of the global TB epidemic. This includes simulating a disease that affects age groups and other risk groups differently, has varying levels of infectiousness depending upon the organ involved and varying outcomes from treatment depending on the drug resistance pattern of the infecting strain.We adopted sound basic principles of software engineering to develop a modular software platform for simulation of TB control interventions ("AuTuMN"). These included object-oriented programming, logical linkage between modules and consistency of code syntax and variable naming. The underlying transmission dynamic model incorporates optional stratification by age, risk group, strain and organ involvement, while our approach to simulating time-variant programmatic parameters better captures the historical progression of the epidemic. An economic model is overlaid upon this epidemiological model which facilitates comparison between new and existing technologies. A "Model runner" module allows for predictions of future disease burden trajectories under alternative scenario situations, as well as uncertainty, automatic calibration, cost-effectiveness and optimisation. The model has now been used to guide TB control strategies across a range of settings and countries, with our modular approach enabling repeated application of the tool without the need for extensive modification for each application.The modular construction of the platform minimises errors, enhances readability and collaboration between multiple programmers and enables rapid adaptation to answer questions in a broad range of contexts without the need for extensive re-programming. Such features are particularly important in simulating an epidemic as complex and diverse as TB.

SUBMITTER: Trauer JM

PROVIDER: S-EPMC5547473 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:BackgroundProcessing and pattern recognition of myoelectric signals have been at the core of prosthetic control research in the last decade. Although most studies agree on reporting the accuracy of predicting predefined movements, there is a significant amount of study-dependent variables that make high-resolution inter-study comparison practically impossible. As an effort to provide a common research platform for the development and evaluation of algorithms in prosthetic control, we introduce BioPatRec as open source software. BioPatRec allows a seamless implementation of a variety of algorithms in the fields of (1) Signal processing; (2) Feature selection and extraction; (3) Pattern recognition; and, (4) Real-time control. Furthermore, since the platform is highly modular and customizable, researchers from different fields can seamlessly benchmark their algorithms by applying them in prosthetic control, without necessarily knowing how to obtain and process bioelectric signals, or how to produce and evaluate physically meaningful outputs.ResultsBioPatRec is demonstrated in this study by the implementation of a relatively new pattern recognition algorithm, namely Regulatory Feedback Networks (RFN). RFN produced comparable results to those of more sophisticated classifiers such as Linear Discriminant Analysis and Multi-Layer Perceptron. BioPatRec is released with these 3 fundamentally different classifiers, as well as all the necessary routines for the myoelectric control of a virtual hand; from data acquisition to real-time evaluations. All the required instructions for use and development are provided in the online project hosting platform, which includes issue tracking and an extensive "wiki". This transparent implementation aims to facilitate collaboration and speed up utilization. Moreover, BioPatRec provides a publicly available repository of myoelectric signals that allow algorithms benchmarking on common data sets. This is particularly useful for researchers lacking of data acquisition hardware, or with limited access to patients.ConclusionsBioPatRec has been made openly and freely available with the hope to accelerate, through the community contributions, the development of better algorithms that can potentially improve the patient's quality of life. It is currently used in 3 different continents and by researchers of different disciplines, thus proving to be a useful tool for development and collaboration.

Project description:Gas-liquid reactions are poorly explored in the context of nanomaterials synthesis, despite evidence of significant effects of dissolved gas on nanoparticle properties. This applies to the aqueous synthesis of iron oxide nanoparticles, where gaseous reactants can influence reaction rate, particle size and crystal structure. Conventional batch reactors offer poor control of gas-liquid mass transfer due to lack of control on the gas-liquid interface and are often unsafe when used at high pressure. This work describes the design of a modular flow platform for the water-based synthesis of iron oxide nanoparticles through the oxidative hydrolysis of Fe2+ salts, targeting magnetic hyperthermia applications. Four different reactor systems were designed through the assembly of two modular units, allowing control over the type of gas dissolved in the solution, as well as the flow pattern within the reactor (single-phase and liquid-liquid two-phase flow). The two modular units consisted of a coiled millireactor and a tube-in-tube gas-liquid contactor. The straightforward pressurization of the system allows control over the concentration of gas dissolved in the reactive solution and the ability to operate the reactor at a temperature above the solvent boiling point. The variables controlled in the flow system (temperature, flow pattern and dissolved gaseous reactants) allowed full conversion of the iron precursor to magnetite/maghemite nanocrystals in just 3 min, as compared to several hours normally employed in batch. The single-phase configuration of the flow platform allowed the synthesis of particles with sizes between 26.5 nm (in the presence of carbon monoxide) and 34 nm. On the other hand, the liquid-liquid two-phase flow reactor showed possible evidence of interfacial absorption, leading to particles with different morphology compared to their batch counterpart. When exposed to an alternating magnetic field, the particles produced by the four flow systems showed ILP (intrinsic loss parameter) values between 1.2 and 2.7 nHm2/kg. Scale up by a factor of 5 of one of the configurations was also demonstrated. The scaled-up system led to the synthesis of nanoparticles of equivalent quality to those produced with the small-scale reactor system. The equivalence between the two systems is supported by a simple analysis of the transport phenomena in the small and large-scale setups.

Project description:BackgroundThrough genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms.ResultsIn this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed.ConclusionWe show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

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Modular programming for tuberculosis control, the "AuTuMN" platform.

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