Use of a continuous culture fermentation system to investigate the effect of GanedenBC30 (Bacillus coagulans GBI-30, 6086) supplementation on pathogen survival in the human gut microbiota

Single-stage continuous fermentation systems were employed to examine the effects of GanedenBC30 supplementation on the human gastrointestinal microbiota in relation to pathogen challenge in vitro. Denaturing gradient gel electrophoresis analysis demonstrated that GanedenBC30 supplementation modified the microbial profiles in the fermentation systems compared with controls, with profiles clustering according to treatment. Overall, GanedenBC30 supplementation did not elicit major changes in bacterial population counts in vitro, although notably higher Bcoa191 counts were seen following probiotic supplementation (compared to the controls). Pathogen challenge did not elicit significant modification of the microbial counts in vitro, although notably higher Clit135 counts were seen in the control system post-Clostridium difficile challenge than in the corresponding GanedenBC30-supplemented systems. Sporulation appears to be associated with the anti-microbial activity of GanedenBC30, suggesting that a bi-modal lifecycle of GanedenBC30 in vivo may lead to anti-microbial activity in distal regions of the gastrointestinal tract.

Rifaximin modulates the colonic microbiota of patients with Crohn's disease: an in vitro approach using a continuous culture colonic model system.

Rifaximin, a rifamycin derivative, has been reported to induce clinical remission of active Crohn's disease (CD), a chronic inflammatory bowel disorder. In order to understand how rifaximin affects the colonic microbiota and its metabolism, an in vitro human colonic model system was used in this study. We investigated the impact of the administration of 1800 mg/day of rifaximin on the faecal microbiota of four patients affected by colonic active CD [Crohn's disease activity index (CDAI > 200)] using a continuous culture colonic model system. We studied the effect of rifaximin on the human gut microbiota using fluorescence in situ hybridization, quantitative PCR and PCR–denaturing gradient gel electrophoresis. Furthermore, we investigated the effect of the antibiotic on microbial metabolic profiles, using 1H-NMR and solid phase microextraction coupled with gas chromatography/mass spectrometry, and its potential genotoxicity and cytotoxicity, using Comet and growth curve assays. Rifaximin did not affect the overall composition of the gut microbiota, whereas it caused an increase in concentration of Bifidobacterium, Atopobium and Faecalibacterium prausnitzii. A shift in microbial metabolism was observed, as shown by increases in short-chain fatty acids, propanol, decanol, nonanone and aromatic organic compounds, and decreases in ethanol, methanol and glutamate. No genotoxicity or cytotoxicity was attributed to rifaximin, and conversely rifaximin was shown to have a chemopreventive role by protecting against hydrogen peroxide-induced DNA damage. We demonstrated that rifaximin, while not altering the overall structure of the human colonic microbiota, increased bifidobacteria and led to variation of metabolic profiles associated with potential beneficial effects on the host.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Discrete time representation of continuous time ARMA processes

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.

Novel on-line sensor technology for continuous monitoring of milk coagulation and whey separation in cheese making

The cheese industry has continually sought a robust method to monitor milk coagulation. Measurement of whey separation is also critical to control cheese moisture content, which affects quality. The objective of this study was to demonstrate that an online optical sensor detecting light backscatter in a vat could be applied to monitor both coagulation and syneresis during cheesemaking. A prototype sensor having a large field of view (LFV) relative to curd particle size was constructed. Temperature, cutting time, and calcium chloride addition were varied to evaluate the response of the sensor over a wide range of coagulation and syneresis rates. The LFV sensor response was related to casein micelle aggregation and curd firming during coagulation and to changes in curd moisture and whey fat contents during syneresis. The LFV sensor has potential as an online, continuous sensor technology for monitoring both coagulation and syneresis during cheesemaking.

Continuous prewarping of time for multiuser interactive virtual environments

Dynamic multi-user interactions in a single networked virtual environment suffer from abrupt state transition problems due to communication delays arising from network latency--an action by one user only becoming apparent to another user after the communication delay. This results in a temporal suspension of the environment for the duration of the delay--the virtual world `hangs'--followed by an abrupt jump to make up for the time lost due to the delay so that the current state of the virtual world is displayed. These discontinuities appear unnatural and disconcerting to the users. This paper proposes a novel method of warping times associated with users to ensure that each user views a continuous version of the virtual world, such that no hangs or jumps occur despite other user interactions. Objects passed between users within the environment are parameterized, not by real time, but by a virtual local time, generated by continuously warping real time. This virtual time periodically realigns itself with real time as the virtual environment evolves. The concept of a local user dynamically warping the local time is also introduced. As a result, the users are shielded from viewing discontinuities within their virtual worlds, consequently enhancing the realism of the virtual environment.

Root growth models: towards a new generation of continuous approaches

Models of root system growth emerged in the early 1970s, and were based on mathematical representations of root length distribution in soil. The last decade has seen the development of more complex architectural models and the use of computer-intensive approaches to study developmental and environmental processes in greater detail. There is a pressing need for predictive technologies that can integrate root system knowledge, scaling from molecular to ensembles of plants. This paper makes the case for more widespread use of simpler models of root systems based on continuous descriptions of their structure. A new theoretical framework is presented that describes the dynamics of root density distributions as a function of individual root developmental parameters such as rates of lateral root initiation, elongation, mortality, and gravitropsm. The simulations resulting from such equations can be performed most efficiently in discretized domains that deform as a result of growth, and that can be used to model the growth of many interacting root systems. The modelling principles described help to bridge the gap between continuum and architectural approaches, and enhance our understanding of the spatial development of root systems. Our simulations suggest that root systems develop in travelling wave patterns of meristems, revealing order in otherwise spatially complex and heterogeneous systems. Such knowledge should assist physiologists and geneticists to appreciate how meristem dynamics contribute to the pattern of growth and functioning of root systems in the field.

Measuring the consumer benefits of improving farm animal welfare to inform welfare labelling

Policy makers in the European Union are envisioning the introduction of a community farm animal welfare label which would allow consumers to align their consumption habits with their farm animal welfare preferences. For welfare labelling to be viable the market for livestock products produced to higher welfare standards has to be sufficiently segmented with consumers having sufficiently distinct and behaviourally consistent preferences. The present study investigates consumers’ preferences for meat produced to different welfare standards using a hypothetical welfare score. Data is obtained from a contingent valuation study carried out in Britain. The ordered probit model was estimated using Bayesian inference to obtain mean willingness to pay. We find decreasing marginal WTP as animal welfare levels increase and that people’s preferences for different levels of farm animal welfare are sufficiently differentiated making the introduction of a labelling scheme in the form of a certified rating system appear feasible.

On variational data assimilation in continuous time

Variational data assimilation in continuous time is revisited. The central techniques applied in this paper are in part adopted from the theory of optimal nonlinear control. Alternatively, the investigated approach can be considered as a continuous time generalization of what is known as weakly constrained four-dimensional variational assimilation (4D-Var) in the geosciences. The technique allows to assimilate trajectories in the case of partial observations and in the presence of model error. Several mathematical aspects of the approach are studied. Computationally, it amounts to solving a two-point boundary value problem. For imperfect models, the trade-off between small dynamical error (i.e. the trajectory obeys the model dynamics) and small observational error (i.e. the trajectory closely follows the observations) is investigated. This trade-off turns out to be trivial if the model is perfect. However, even in this situation, allowing for minute deviations from the perfect model is shown to have positive effects, namely to regularize the problem. The presented formalism is dynamical in character. No statistical assumptions on dynamical or observational noise are imposed.