An investigation of the feasibility of recycling deicing materials at Munich airport

The growing public awareness and sensitiveness towards environmental protection increases significantly the pressure upon the air transport industry to implement regulatory measures for the operation of both airports and aircraft. With regard to the feasibility of recycling deicing materials and the requirement to achieve compatibility between a deicing concept and a fluid recycling concept, many airports are isolated with insufficient guidelines for developing an appropriate decision making process. Regretably at present only informations exists, which deals with the dedicated issues and problems concerning a1rcraft deicmg, airport deicing and the disposal of fluids. Also, differing international and national regulations concerning environmental protection have impeded the development of generic strategies. As Munich International Airport has Implemented a specialized concept of aircraft/airport deicing and fluid recycling with the opening of the airport m 1992, the decision to investigate its operational, environmental and economic performance in this thesis was simple and obvious. However, aircraft/airport deicing is an international issue, which affects many airport and airlines around the world. Consequently, a generic strategy would be of general interest. Although the Munich case is the basis for this thesis,international operational aspects and environmental issues are also discussed with a view to drawing conclusions for the establishment of a generic strategy. The major conclusions concern the need to improve existing environmental legislation and to harmonize these legislative measures in order to achieve a general applicable international standard worldwide. There is no perfect alternative- no one solution to fit every size of airport. Differing international environmental regulations and. standards concerning fluid disposal and environmental impact demand diversified investigations which subsequently may lead to totally different solutions for an individual airport operator. The recommendations and suggested generic strategies contained in this thesis are only to be seen as a guideline for any decision making process an airport operator may suddenly be confronted with

Accuracy of phenotyping generated datasets in a sequential or integrated manner.

<p>The accuracy associated with sequential fitting Gaussians to fluorescence data to obtain cell counts for each generation (blue) and integrated fitting of the fcyton model to fluorescence data directly using fitted fluorescence parameters as adaptors (purple) was determined for 1,000 sets of randomly generated realistic CFSE time courses (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067620#pone.0067620.s010" target="_blank">Tables S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067620#pone.0067620.s011" target="_blank">S4</a>). (A) Average percent error in generational cell counts normalized to the maximum generational cell count for each time course. Numbers indicate an error ≥ 0.5%. (B) Analysis of the error associated with determining key fcyton cellular parameters. Box plots represent 5,25,50,75, and 95 percentile values. Outliers are not shown. For a comparison of all 12 parameters see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067620#pone.0067620.s001" target="_blank">Figure S1</a> (blue) and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067620#pone.0067620.s002" target="_blank">Figure S2</a> (purple).</p

Proposed integrated phenotyping approach (FlowMax).

<p>CFSE flow-cytometry time series are preprocessed to create one-dimensional fluorescence histograms that are used to determine the cell proliferation parameters for each time point, using the parameters of the previous time points as added constraints (step 1). Fluorescence parameters are then used to extend a cell population model and allow for direct training of the cell population parameters on the fluorescence histograms (step 2). To estimate solution sensitivity and redundancy, step 2 is repeated many times, solutions are filtered by score, parameter sensitivities are determined for each solution, non-redundant maximum-likelihood parameter ranges are found after clustering, and a final filtering step eliminates clusters representing poor solutions (step 3).</p

A prototypical negative feedback module.

<p>(A) In this simple model of negative feedback control, an activator X is constitutively produced but catalytically degraded by an inhibitor, Y. Y is constitutively degraded but its synthesis requires X. Each of these four reactions is modeled using mass action kinetics. To stimulate the model and activate X, the steady-state abundance of Y is instantaneously depleted. (B) In response to stimulation, the abundance of X increases until activator-induced synthesis of Y forces a return to steady-state. This response can be characterized by , the maximum abundance of X following stimulation, and , the time at which is observed. Parameters were chosen for this model such that the steady state abundances of X and Y equal one arbitrary unit and the stimulus-induced amplitude of X is at time . The rates of activator synthesis (C), inhibitor synthesis (D), activator degradation (E), and inhibitor degradation (F) were multiplied by (gray) to (blue) prior to stimulation as described above. For each multiplier, the dynamics of the activator response are plotted on the right. Similar plots were generated by multiplying the flux of the activator (G), and the flux of the inhibitor (H), as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002932#s4" target="_blank">Methods</a>.</p

FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses

<div><p>The immune response is a concerted dynamic multi-cellular process. Upon infection, the dynamics of lymphocyte populations are an aggregate of molecular processes that determine the activation, division, and longevity of individual cells. The timing of these single-cell processes is remarkably widely distributed with some cells undergoing their third division while others undergo their first. High cell-to-cell variability and technical noise pose challenges for interpreting popular dye-dilution experiments objectively. It remains an unresolved challenge to avoid under- or over-interpretation of such data when phenotyping gene-targeted mouse models or patient samples. Here we develop and characterize a computational methodology to parameterize a cell population model in the context of noisy dye-dilution data. To enable objective interpretation of model fits, our method estimates fit sensitivity and redundancy by stochastically sampling the solution landscape, calculating parameter sensitivities, and clustering to determine the maximum-likelihood solution ranges. Our methodology accounts for both technical and biological variability by using a cell fluorescence model as an adaptor during population model fitting, resulting in improved fit accuracy without the need for <i>ad hoc</i> objective functions. We have incorporated our methodology into an integrated phenotyping tool, FlowMax, and used it to analyze B cells from two NFκB knockout mice with distinct phenotypes; we not only confirm previously published findings at a fraction of the expended effort and cost, but reveal a novel phenotype of nfkb1/p105/50 in limiting the proliferative capacity of B cells following B-cell receptor stimulation. In addition to complementing experimental work, FlowMax is suitable for high throughput analysis of dye dilution studies within clinical and pharmacological screens with objective and quantitative conclusions.</p></div

A Protein Turnover Signaling Motif Controls the Stimulus-Sensitivity of Stress Response Pathways

<div><p>Stimulus-induced perturbations from the steady state are a hallmark of signal transduction. In some signaling modules, the steady state is characterized by rapid synthesis and degradation of signaling proteins. Conspicuous among these are the p53 tumor suppressor, its negative regulator Mdm2, and the negative feedback regulator of NFκB, IκBα. We investigated the physiological importance of this turnover, or flux, using a computational method that allows flux to be systematically altered independently of the steady state protein abundances. Applying our method to a prototypical signaling module, we show that flux can precisely control the dynamic response to perturbation. Next, we applied our method to experimentally validated models of p53 and NFκB signaling. We find that high p53 flux is required for oscillations in response to a saturating dose of ionizing radiation (IR). In contrast, high flux of Mdm2 is not required for oscillations but preserves p53 sensitivity to sub-saturating doses of IR. In the NFκB system, degradation of NFκB-bound IκB by the IκB kinase (IKK) is required for activation in response to TNF, while high IKK-independent degradation prevents spurious activation in response to metabolic stress or low doses of TNF. Our work identifies flux pairs with opposing functional effects as a signaling motif that controls the stimulus-sensitivity of the p53 and NFκB stress-response pathways, and may constitute a general design principle in signaling pathways.</p> </div

Effects of flux on the dynamic response to stimulation.

<p>(A) The magnitude of the activator flux is varied between (light gray) and (dark gray) times its nominal steady-state value prior to stimulation. The peak amplitude of X in response to stimulation is observed to increase with the flux of X while the time at which the peak occurs is observed to decrease. Representative profiles of the activator at low, wildtype, and high values of the flux are shown at right. The dashed red line indicates the nominal wildtype response. (B) The magnitude of the inhibitor flux is varied between and times its nominal steady-state value prior to stimulation. Both and are observed to decrease. (C) The fluxes of both X and Y are varied simultaneously between and times their nominal wildtype values. As a result, is held constant while is reduced. (D) The magnitude of the inhibitor flux is varied between and times its nominal steady-state value prior to stimulation. For each value of this flux, the value of activator flux is calculated such that is held constant. As in row 2 above, is observed to decrease as the magnitude of the flux of Y increases.</p

Effects of IκB flux on the NFκB response to stimulation.

<p>(A) The productive flux of IκB was varied between and times its wildtype value prior to stimulation by TNF (light gray to dark gray), and the resulting NFκB response values and plotted in columns 2 and 3. Representative nuclear NFκB profiles for low, moderate, wildtype, and high values of the flux multiplier are shown at right. Again, the wildtype productive flux is indicated by the dashed line in red. (B) The futile flux of IκB was varied between and times its wildtype value prior to stimulation by TNF and the resulting NFκB response values and plotted in columns 2 and 3. (C) and (D) As rows 1 and 2, above, but the response to UV stimulation is plotted instead of TNF.</p

A paired positive (+) and negative (−) flux motif controls stimulus-sensitivity in the p53 and NFkB stress response pathways.

<p>(<b>A</b>) For p53, the (+) flux is formed by the synthesis and degradation of p53 itself. The (−) flux is formed by synthesis and degradation of Mdm2. Together these fluxes control the sensitivity of p53 to IR-stimulation, which acts by inducing the synthesis of p53 and the degradation of Mdm2. (B) For NFkB, the (+) flux is formed by association and dissociation of NFkB from its negative regulator, IκB. The (−) flux is formed by synthesis and degradation of IκB. These fluxes control the sensitivity of NFkB to TNF-stimulation, which induces the dissociation of NFκB from IκB, and UV-stimulation, which inhibits the synthesis of IκB.</p

Comparison of FlowMax to the Cyton Calculator.

<p>The Cyton Calculator <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067620#pone.0067620-Hawkins3" target="_blank">[9]</a> and a computational tool implementing our methodology, “FlowMax,” were used to train the cyton model with log-normally distributed division and death times on a CFSE time course of wildtype B cells stimulated with lipopolysaccharides (LPS). The best-fit generational cell counts were input to the Cyton Calculator. (A) Visual summary of solution quality estimation pipeline implemented as part of FlowMax. Candidate parameter sets are filtered by the normalized % area difference score, parameter sensitivity ranges are calculated, parameter sensitivity ranges are clustered to reveal non-redundant maximum-likelihood parameter ranges (red ranges). Jagged lines represent the sum of uniform parameter distributions in each cluster. (B) Best fit cyton model parameters determined using the Cyton Calculator (blue dots) and our phenotyping tool, FlowMax (square red individual fits with sensitivity ranges represented by error bars and square green weighted cluster averages with error bars representing the intersection of parameter sensitivity ranges for 41 solutions in the only identified cluster). (C) Plots of Fs (the fraction of cells dividing to the next generation), and log-normal distributions for the time to divide and die of undivided and dividing cells sampled uniformly from best-fit cluster ranges in (B). (D) Generational (colors) and total cell counts (black) are plotted as a function of time for 250 cyton parameter sets sampled uniformly from the intersection of best-fit cluster parameter ranges. Red dots show average experimental cell counts for each time point. Error bars show standard deviation for duplicate runs.</p