891 resultados para bioreactor chambers


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Water to air methane emissions from freshwater reservoirs can be dominated by sediment bubbling (ebullitive) events. Previous work to quantify methane bubbling from a number of Australian sub-tropical reservoirs has shown that this can contribute as much as 95% of total emissions. These bubbling events are controlled by a variety of different factors including water depth, surface and internal waves, wind seiching, atmospheric pressure changes and water levels changes. Key to quantifying the magnitude of this emission pathway is estimating both the bubbling rate as well as the areal extent of bubbling. Both bubbling rate and areal extent are seldom constant and require persistent monitoring over extended time periods before true estimates can be generated. In this paper we present a novel system for persistent monitoring of both bubbling rate and areal extent using multiple robotic surface chambers and adaptive sampling (grazing) algorithms to automate the quantification process. Individual chambers are self-propelled and guided and communicate between each other without the need for supervised control. They can maintain station at a sampling site for a desired incubation period and continuously monitor, record and report fluxes during the incubation. To exploit the methane sensor detection capabilities, the chamber can be automatically lowered to decrease the head-space and increase concentration. The grazing algorithms assign a hierarchical order to chambers within a preselected zone. Chambers then converge on the individual recording the highest 15 minute bubbling rate. Individuals maintain a specified distance apart from each other during each sampling period before all individuals are then required to move to different locations based on a sampling algorithm (systematic or adaptive) exploiting prior measurements. This system has been field tested on a large-scale subtropical reservoir, Little Nerang Dam, and over monthly timescales. Using this technique, localised bubbling zones on the water storage were found to produce over 50,000 mg m-2 d-1 and the areal extent ranged from 1.8 to 7% of the total reservoir area. The drivers behind these changes as well as lessons learnt from the system implementation are presented. This system exploits relatively cheap materials, sensing and computing and can be applied to a wide variety of aquatic and terrestrial systems.

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This study reports on an original concept of additive manufacturing for the fabrication of tissue engineered constructs (TEC), offering the possibility of concomitantly manufacturing a customized scaffold and a bioreactor chamber to any size and shape. As a proof of concept towards the development of anatomically relevant TECs, this concept was utilized for the design and fabrication of a highly porous sheep tibia scaffold around which a bioreactor chamber of similar shape was simultaneously built. The morphology of the bioreactor/scaffold device was investigated by micro-computed tomography and scanning electron microscopy confirming the porous architecture of the sheep tibiae as opposed to the non-porous nature of the bioreactor chamber. Additionally, this study demonstrates that both the shape, as well as the inner architecture of the device can significantly impact the perfusion of fluid within the scaffold architecture. Indeed, fluid flow modelling revealed that this was of significant importance for controlling the nutrition flow pattern within the scaffold and the bioreactor chamber, avoiding the formation of stagnant flow regions detrimental for in vitro tissue development. The bioreactor/scaffold device was dynamically seeded with human primary osteoblasts and cultured under bi-directional perfusion for two and six weeks. Primary human osteoblasts were observed homogenously distributed throughout the scaffold, and were viable for the six week culture period. This work demonstrates a novel application for additive manufacturing in the development of scaffolds and bioreactors. Given the intrinsic flexibility of the additive manufacturing technology platform developed, more complex culture systems can be fabricated which would contribute to the advances in customized and patient-specific tissue engineering strategies for a wide range of applications.

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Mammographic density (MD) is a strong risk factor for breast cancer. It is altered by exogenous endocrine treatments, including hormone replacement therapy and Tamoxifen. Such agents also modify breast cancer (BC) risk. However, the biomolecular basis of how systemic endocrine therapy modifies MD and MD-associated BC risk is poorly understood. This study aims to determine whether our xenograft biochamber model can be used to study the effectiveness of therapies aimed at modulating MD, by examine the effects of Tamoxifen and oestrogen on histologic and radiographic changes in high and low MD tissues maintained within the biochamber model. High and low MD human tissues were precisely sampled under radiographic guidance from prophylactic mastectomy fresh specimens of high-risk women, then inserted into separate vascularized murine biochambers. The murine hosts were concurrently implanted with Tamoxifen, oestrogen or placebo pellets, and the high and low MD biochamber tissues maintained in the murine host environment for 3 months, before the high and low MD biochamber tissues were harvested for histologic and radiographic analyses. The radiographic density of high MD tissue maintained in murine biochambers was decreased in Tamoxifen-treated mice compared to oestrogen-treated mice (p = 0.02). Tamoxifen treatment of high MD tissue in SCID mice led to a decrease in stromal (p = 0.009), and an increase in adipose (p = 0.023) percent areas, compared to placebo-treated mice. No histologic or radiographic differences were observed in low MD biochamber tissue with any treatment. High MD biochamber tissues maintained in mice implanted with Tamoxifen, oestrogen or placebo pellets had dynamic and measurable histologic compositional and radiographic changes. This further validates the dynamic nature of the MD xenograft model, and suggests the biochamber model may be useful for assessing the underlying molecular pathways of Tamoxifen-reduced MD, and in testing of other pharmacologic interventions in a preclinical model of high MD.

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Large numbers of Mesenchymal stem/stromal cells (MSCs) are required for clinical relevant doses to treat a number of diseases. To economically manufacture these MSCs, an automated bioreactor system will be required. Herein we describe the development of a scalable closed-system, packed bed bioreactor suitable for large-scale MSCs expansion. The packed bed was formed from fused polystyrene pellets that were air plasma treated to endow them with a surface chemistry similar to traditional tissue culture plastic. The packed bed was encased within a gas permeable shell to decouple the medium nutrient supply and gas exchange. This enabled a significant reduction in medium flow rates, thus reducing shear and even facilitating single pass medium exchange. The system was optimised in a small-scale bioreactor format (160 cm2) with murine-derived green fluorescent protein-expressing MSCs, and then scaled-up to a 2800 cm2 format. We demonstrated that placental derived MSCs could be isolated directly within the bioreactor and subsequently expanded. Our results demonstrate that the closed system large-scale packed bed bioreactor is an effective and scalable tool for large-scale isolation and expansion of MSCs.

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It is essential to have a thorough understanding of the sources and sinks of oxidized nitrogen (NOy) in the atmosphere, since it has a strong influence on the tropospheric chemistry and the eutrophication of ecosystems. One unknown component in the balance of gaseous oxidized nitrogen is vegetation. Plants absorb nitrogenous species from the air via the stomata, but it is not clear whether plants can also emit them at low ambient concentrations. The possible emissions are small and difficult to measure. The aim of this thesis was to analyse an observation made in southern Finland at the SMEAR II station: solar ultraviolet radiation (UV) induced NOy emissions in chambers measuring the gas exchange of Scots pine (Pinus sylvestris L.) shoots. Both measuring and modelling approaches were used in the study. The measurements were performed under noncontrolled field conditions at low ambient NOy concentrations. The chamber blank i.e. artefact NOy emissions from the chamber walls, was dependent on the UV irradiance and increased with time after renewing the Teflon film on chamber surfaces. The contribution of each pine shoot to the total NOy emissions in the chambers was determined by testing whether the emissions decrease when the shoots are removed from their chambers. Emissions did decrease, but only when the chamber interior was exposed to UV radiation. It was concluded that also the pine shoots emit NOy. The possible effects of transpiration on the chamber blank are discussed in the summary part of the thesis, based on previously unpublished data. The possible processes underlying the UV-induced NOy emissions were reviewed. Surface reactions were more likely than metabolic processes. Photolysis of nitrate deposited on the needles may have generated the NOy emissions; the measurements supported this hypothesis. In that case, the emissions apparently would consist mainly of nitrogen dioxide (NO2), nitric oxide (NO) and nitrous acid (HONO). Within studies on NOy exchange of plants, the gases most frequently studied are NO2 and NO (=NOx). In the present work, the implications of the emissions for the NOx exchange of pine were analysed with a model including both NOy emissions and NOy absorption. The model suggested that if the emissions exist, pines can act as an NOx source rather than a sink, even under relatively high ambient concentrations.

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This research project investigated a bioreactor system capable of high density cell growth intended for use in regenerative medicine and protein production. The bioreactor was based on a drip-perfusion concept and constructed with minimal costs, readily available components, and straightforward processes for usage. This study involved the design, construction, and testing of the bioreactor where the results showed promising three dimensional cell growth within a polymer structure. The accessibility of this equipment and the capability of high density, three dimensional cell growth would be suitable for future research in pharmaceutical drug manufacturing, and human organ and tissue regeneration.

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A mathematical model is developed to simulate oxygen consumption, heat generation and cell growth in solid state fermentation (SSF). The fungal growth on the solid substrate particles results in the increase of the cell film thickness around the particles. The model incorporates this increase in the biofilm size which leads to decrease in the porosity of the substrate bed and diffusivity of oxygen in the bed. The model also takes into account the effect of steric hindrance limitations in SSF. The growth of cells around single particle and resulting expansion of biofilm around the particle is analyzed for simplified zero and first order oxygen consumption kinetics. Under conditions of zero order kinetics, the model predicts upper limit on cell density. The model simulations for packed bed of solid particles in tray bioreactor show distinct limitations on growth due to simultaneous heat and mass transport phenomena accompanying solid state fermentation process. The extent of limitation due to heat and/or mass transport phenomena is analyzed during different stages of fermentation. It is expected that the model will lead to better understanding of the transport processes in SSF, and therefore, will assist in optimal design of bioreactors for SSF.

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Cryogel matrices composed of different polymeric blends were synthesized, yielding a unique combination of hydrophilicity and hydrophobicity with the presence or absence of charged surface. Four such cryogel matrices composed of polyacrylamide-chitosan (PAAC), poly(N-isopropylacrylamide)-chitosan, polyacrylonitrile (PAN), and poly(N-isopropylacrylamide) were tested for growth of different hybridoma cell lines and production of antibody in static culture. All the matrices were capable for the adherence of hybridoma cell lines 6A4D7, B7B10, and H9E10 to the polymeric surfaces as well as for the efficient monoclonal antibody (mAb) production. PAAC proved to be relatively better in terms of both mAb production and cell growth. Further, PAAC cryogel was designed into three different formats, monolith, disks, and beads, and used as packing material for packed-bed bioreactor. Longterm cultivation of 6A4D7 cell line on PAAC cryogel scaffold in all the three formats could be successfully done for a period of 6 weeks under static conditions. Continuous packed-bed bioreactor was setup using 6A4D7 hybridoma cell line in the three reactor formats. The reactors ran continuously for a period of 60 days during which mAb production and metabolism of cells in the bioreactors were monitored periodically. The monolith bioreactor performed most efficiently over a period of 60 days and produced a total of 57.5 mg of antibody in the first 30 days (in 500 mL) with a highest concentration of 115 mu g mL(-1), which is fourfold higher than t-flask culture. The results demonstrate that appropriate chemistry and geometry of the bioreactor matrix for cell growth and immobilization can enhance the reactor productivity. (C) 2010 American Institute of Chemical Engineers Biotechnol. Prog., 27: 170-180, 2011

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An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

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A mathematical model describing the dynamics of mammalian cell growth in hollow fibre bioreactor operated in closed shell mode is developed. Mammalian cells are assumed to grow as an expanding biofilm in the extra-capillary space surrounding the fibre. Diffusion is assumed to be the dominant process in the radial direction while axial convection dominates in the lumen of the bioreactor. The transient simulation results show that steep gradients in the cell number are possible under the condition of substrate limitation. The precise conditions which result in nonuniform growth of cells along the length of the bioreactor are delineated. The effect of various operating conditions, such as substrate feed rate, length of the bioreactor and diffusivity of substrate in different regions of the bioreactor, on the bioreactor performance are evaluated in terms of time required to attain the steady-state. The rime of growth is introduced as a measure of effectiveness factor for the bioreactor and is found to be dependent on two parameters, a modified Peclet number and a Thiele modulus. Diffusion, reaction and/or convection control regimes are identified based on these two parameters. The model is further extended to include dual substrate growth limitations, and the relative growth limiting characteristics of two substrates are evaluated. (C) 1997 Elsevier Science Ltd.

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Stirred tank bioreactors, employed in the production of a variety of biologically active chemicals, are often operated in batch, fed-batch, and continuous modes of operation. The optimal design of bioreactor is dependent on the kinetics of the biological process, as well as the performance criteria (yield, productivity, etc.) under consideration. In this paper, a general framework is proposed for addressing the two key issues related to the optimal design of a bioreactor, namely, (i) choice of the best operating mode and (ii) the corresponding flow rate trajectories. The optimal bioreactor design problem is formulated with initial conditions and inlet and outlet flow rate trajectories as decision variables to maximize more than one performance criteria (yield, productivity, etc.) as objective functions. A computational methodology based on genetic algorithm approach is developed to solve this challenging multiobjective optimization problem with multiple decision variables. The applicability of the algorithm is illustrated by solving two challenging problems from the bioreactor optimization literature.

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The stability of a bioreactor landfill slope is influenced by the quantity and method of leachate recirculation as well as on the degree of decomposition. Other factors include properties variation of waste material and geometrical configurations, i.e., height and slope of landfills. Conventionally, the stability of slopes is evaluated using factor of safety approach, in which the variability in the engineering properties of MSW is not considered directly and stability issues are resolved from past experiences and good engineering judgments. On the other hand, probabilistic approach considers variability in mathematical framework and provides stability in a rational manner that helps in decision making. The objective of the present study is to perform a parametric study on the stability of a bioreactor landfill slope in probabilistic framework considering important influencing factors, such as, variation in MSW properties, amount of leachate recirculation, and age of degradation, in a systematic manner. The results are discussed in the light of existing relevant regulations, design and operation issues.