Multiple Model Parameters Research Articles

Cost Effectiveness of External Beam Radiation Therapy versus Percutaneous Image-Guided Cryoablation for Palliation of Uncomplicated Bone Metastases

PurposeTo evaluate the cost effectiveness of incorporating cryoablation in the treatment regimens for uncomplicated bone metastases using radiation therapy (RT) in single-fraction RT (SFRT) or multiple-fraction RT (MFRT) regimens. Materials and MethodsA Markov model was constructed using 1-month cycles over a lifetime horizon to compare the cost effectiveness of multiple strategies, including RT followed by RT (RT-RT) for recurrent pain, RT followed by cryoablation (RT-ablation), and cryoablation followed by RT (ablation-RT). RT-RT consisted of 8 Gy in 1 fraction/8 Gy in 1 fraction (SFRT-SFRT) and 30 Gy in 10 fractions/20 Gy in 5 fractions (MFRT-MFRT). Probabilities and utilities were extracted from a search of the medical literature. Costs were calculated from a payer perspective using 2017 Medicare reimbursement in an outpatient setting. Incremental cost effectiveness ratios (ICERs) were calculated using strategies evaluated for willingness-to-pay threshold of $100,000 per quality-adjusted life-year (QALY). To account for model uncertainty, one-way and probabilistic sensitivity analyses were performed. ResultsIn the base case analysis, SFRT-ablation was cost effective relative to SFRT-SFRT at $96,387/QALY. MFRT-ablation was cost effective relative to MFRT-MFRT at $85,576/QALY. Ablation-SFRT and ablation-MFRT were not cost effective with ICERs >$100,000/QALY. In one-way sensitivity analyses, results were highly sensitive to variation in multiple model parameters, including median survival (base: 9 months), with SFRT-SFRT favored at median survival ≤8.7 months. Probabilistic sensitivity analysis examining SFRT-based regimens showed that SFRT-ablation was preferred in 36.9% of simulations at WTP of $100,000/QALY. ConclusionsCryoablation is a potentially cost-effective alternative to reirradiation with RT for recurrent of pain following RT; however, no strategy incorporating initial cryoablation was cost effective.

Thermophysical modelling and parameter estimation of small Solar system bodies via data assimilation

ABSTRACT Deriving thermophysical properties such as thermal inertia from thermal infrared observations provides useful insights into the structure of the surface material on planetary bodies. The estimation of these properties is usually done by fitting temperature variations calculated by thermophysical models to infrared observations. For multiple free model parameters, traditional methods such as least-squares fitting or Markov chain Monte Carlo methods become computationally too expensive. Consequently, the simultaneous estimation of several thermophysical parameters, together with their corresponding uncertainties and correlations, is often not computationally feasible and the analysis is usually reduced to fitting one or two parameters. Data assimilation (DA) methods have been shown to be robust while sufficiently accurate and computationally affordable even for a large number of parameters. This paper will introduce a standard sequential DA method, the ensemble square root filter, for thermophysical modelling of asteroid surfaces. This method is used to re-analyse infrared observations of the MARA instrument, which measured the diurnal temperature variation of a single boulder on the surface of near-Earth asteroid (162173) Ryugu. The thermal inertia is estimated to be 295 ± 18 $\mathrm{J\, m^{-2}\, K^{-1}\, s^{-1/2}}$, while all five free parameters of the initial analysis are varied and estimated simultaneously. Based on this thermal inertia estimate the thermal conductivity of the boulder is estimated to be between 0.07 and 0.12,$\mathrm{W\, m^{-1}\, K^{-1}}$ and the porosity to be between 0.30 and 0.52. For the first time in thermophysical parameter derivation, correlations and uncertainties of all free model parameters are incorporated in the estimation procedure that is more than 5000 times more efficient than a comparable parameter sweep.

Data assimilation for three-dimensional phase-field simulation of dendritic solidification using the local ensemble transform Kalman filter

Data assimilation (DA) based on Bayes' theorem helps improve the accuracy of numerical models and simultaneously enables the estimation of unknown parameters used in the numerical model by combining simulation results with observational data. We applied the local ensemble transform Kalman filter (LETKF), a computationally efficient and accurate DA methodology, to a phase-field model of dendritic solidification in a binary alloy. We demonstrated the efficiency of LETKF through numerical experiments (twin experiments) wherein we estimated the unknown state of the solidification and the model parameters from synthetic observation datasets of a growing dendrite morphology. Results of the twin experiments show that using LETKF we could successfully estimate three-dimensional (3D) time evolution of the solute concentration-field in the liquid phase. Further, we could inversely identify multiple model parameters, including interfacial energy between the solid and liquid phases and the solute diffusion coefficient in the liquid phase only from the 3D morphological information of a growing dendrite. We demonstrated that the LETKF-based DA method is a promising methodology for performing accurate phase-field simulations in conjunction with experimental data.

A multi-center assessment to compare residual allergenicity of partial hydrolyzed whey proteins in a murine model for cow’s milk allergy – Comparison to the single parameter guinea pig model

IntroductionThis 4-center study is part of a project to validate a food allergy murine model for safety testing of hydrolyzed infant formulas. AimThe aim of the current multi-center experiment was to evaluate the residual allergenicity of three partial hydrolyzed whey proteins (pWH) in a multiple-parameter cow’s milk allergy murine model and to compare to the classically used guinea pig model. Previous work showed differences in the magnitude of the allergic response to whey between centers. To get a first insight in the effect of housing on the robustness of the mouse model, microbiota composition of non-sensitized mice was analyzed and compared between centers. MethodsMice were sensitized intragastrically (i.g.) with whey, pWH or eWH using cholera toxin as an adjuvant. In mice, whey-IgE/IgG1, acute allergic symptoms were determined upon whey challenge. Guinea pigs were orally sensitized ad libitum via the drinking water (day 0–37) and challenged intravenously with whey on day 49. The microbial composition in fecal samples was determined in non-sensitized mice in all 4 research centers before and after conduct of the study. ResultsElevated levels of whey-IgG1 were detected in whey-sensitized mice in all centers. Except for pWH-A in center 4, we observed elevated levels of whey-IgE in whey-sensitized mice and mice sensitized with pWH-A, -B, -C. Center 2 was excluded from further analysis because of non-significant IgE levels in the positive control. In contrast to whey-mice, pWH-A treated mice showed no acute skin response, mMCP-1 release or change in body temperature upon whey challenge in all centers, which corresponds with the absence of anaphylactic shock symptoms in both the mouse and guinea pig model. pWH-B and -C induced anaphylactic shock symptoms in the guinea-pig and mice whereas results on the remaining allergic outcomes in mice were inconclusive. No differences in microbiota composition were measured in response to the challenge and Microbiota composition depended on the location of the centers. ConclusionsBoth animal models showed comparable results on the residual allergenicity of partial hydrolyzed whey proteins, but none of the centers was able to differentiate between the residual sensitizing capacities of the pWH-B and -C based on a single elicitation parameter in the murine model. Differences in microbiota composition might contribute to the robustness of the food allergy murine model. For a well-balanced prediction on the potential allergenicity of hydrolyzed infant formulas a multiple murine parameter model is suggested to decrease the risk of false positive or false negative results. A future challenge is to develop an overall scoring system for proper risk assessment, taking all parameters into account.

Cost-effectiveness of Using Kidneys From HCV-Viremic Donors for Transplantation Into HCV-Uninfected Recipients

Less than 4% of patients with kidney failure receive kidney transplants. Although discard rates of hepatitis C virus (HCV)-viremic kidneys are declining, ~39% of HCV-viremic kidneys donated between 2018 and 2019 were discarded. Highly effective antiviral agents are now available to treat chronic HCV infection. Thus, our objective was to examine the cost-effectiveness of transplanting kidneys from HCV-viremic donors into HCV-uninfected recipients. Markov state transition decision model. Data sources include Medline search results, bibliographies from relevant English language articles, Scientific Registry of Transplant Recipients, and the US Renal Data System. US patients receiving maintenance hemodialysis who are on kidney transplant waiting lists. Transplantation with an HCV-unexposed kidney versus transplantation with an HCV-viremic kidney and HCV treatment. Effectiveness measured in quality-adjusted life-years and costs measured in 2018 US dollars. We used a health care system perspective with a lifelong time horizon. In the base-case analysis, transplantation with an HCV-viremic kidney was more effective and less costly than transplantation with an HCV-unexposed kidney because of the longer waiting times for HCV-unexposed kidneys, the substantial excess mortality risk while receiving dialysis, and the high efficacy of direct-acting antiviral agents for HCV infection. Transplantation with an HCV-viremic kidney was also preferred in sensitivity analyses of multiple model parameters. The strategy remained cost-effective unless waiting list time for an HCV-viremic kidney exceeded 3.1 years compared with the base-case value of 1.56 year. Estimates of waiting times for patients willing to accept an HCV-viremic kidney were based on data for patients who received HCV-viremic kidney transplants. Transplanting kidneys from HCV-viremic donors into HCV-uninfected recipients increased quality-adjusted life expectancy and reduced costs compared with a strategy of transplanting kidneys from HCV-unexposed donors.

Compact silicon photonics circuit to extract multiple parameters for process control monitoring

We present a compact interferometer circuit to extract multiple model parameters of on-chip waveguides and directional couplers from optical measurements. The compact design greatly improves the accuracy of extraction with fewer measurements, making it useful for process monitoring and detailed wafer-level variability analysis. We discuss the design requirements and illustrate the extraction using the Restart-CMA-ES global optimization algorithm.

Evolutionary multi-task optimization for parameters extraction of photovoltaic models

As the demand for solar energy increases dramatically, the optimization and control of photovoltaic systems become increasingly important, accurate and reliable parameter identification of photovoltaic models is always required, which proposes an urgent need for accurate and robust algorithms. To this end, many heuristic algorithms have been proposed to extract the parameters of different photovoltaic models. However, they only extract the parameters of one model in a single run, which is inconsistent with the human ability to solve multiple tasks simultaneously and ignores the useful information derived from different models. Therefore, in this paper an evolutionary multi-task optimization algorithm is proposed to extract the parameters of multiple different photovoltaic models simultaneously. To be specific, the helpful information found by the population is transferred through the cross-task crossover to improve the performance in terms of solution quality and convergence rate of the population. The proposed algorithm is evaluated by extracting the parameters of three different models simultaneously, i.e., single diode, double diode, and photovoltaic module model. Comprehensive results demonstrate that the proposed algorithm has better performance with respect to the accuracy and robustness in comparison with other state-of-the-art algorithms.

Identification of Multiple First-Order Continuous-Time Dynamic Models From Special Segments in Historical Data

Continuous-time dynamic models are often required for controller design, process monitoring, and operation optimization. This paper proposes an approach to estimate unknown parameters of multiple first-order continuous-time dynamic models from special segments in historical data. The approach is composed by two main steps. First, special segments are defined as the ones with two ends in steady states and the middle part having significant amplitude variations in transient states; the special segments are found automatically by exploiting a piece-wise linear representation technique from a large amount of historical data samples. Second, static gains are estimated by solving a set of linear equations based on steady-state values of inputs and outputs; sums of time constants and delays are obtained by solving another set of linear equations based on integrals of model output errors from data samples in transient states; the sums are used as an optimization constraint for maximizing the fitness value between measured and simulated outputs, from which separated estimates of time constants and delays are yielded. Numerical examples are provided to illustrate the proposed approach and compare with existing ones.

A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.

The comparative study of in transition zone prostate cancer: diagnostic performance of new intravoxel incoherent motion and diffusion kurtosis imaging models

Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti-parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P 0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P 0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD. Key words: Prostate neoplasms; Benign prostatic hyperplasia; Intravoxel incoherent motion; Diffusion kurtosis imaging

Analysis of mechanical properties for tea stem using grey relational analysis coupled with multiple linear regression

To improve the picking quality of tea plucking machine, the relationship between mechanical properties of tea stem and their influence factors was studied. Tea variety of Zhongcha 108 was selected as test sample to analyze the grey correlations of stem segment number, stem diameter, stem inter-node distance, moisture content, fracture deflection, moment of inertia, young's modulus, hemicellulose content, cellulose content, lignin content to mechanical properties (bending and shearing strength). Further, multiple parameter optimization and model establishment were performed using partial least squares regression (PLSR), principal component analysis (PCA) + multiple linear regression (MLR), grey relational analysis (GRA) + MLR. The results showed that the cellulose content, bending strength, shearing strength, stem diameter and moment of inertia generally increased with stem segment number. For bending and shearing strength, GRA (0.6) + MLR method was suitable to establish the model. The values of adjusted Rc2 and RMSEC of bending strength were 0.8634 and 3.3264 N, and these values of shearing strength were 0.9730 and 0.2573 N. Besides, adjusted Rp2 values of bending and shearing strength were 0.9443 and 0.9125, RMSEP values were 2.8580 N and 0.7211 N, respectively. The GRA-MLR model is accurate and effective method to investigate the mechanical properties of tea stem for designing a suitable cutting equipment.

Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX-Laguerre Multiple Models

In this article, we propose a fault tolerant control for multiple-input multiple-output (MIMO) nonlinear systems via model predictive control. The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models. The latter is obtained by expanding a discrete-time MIMO ARX multiple model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical MIMO ARX multiple models. This parametric complexity reduction still subjects to an optimal choice of the Laguerre poles defining Laguerre bases. The parameter and structure identifications of the MIMO ARX-Laguerre multiple models are achieved by the recursive method and a metaheuristic algorithm, respectively. The proposed model is built from the system input/output observations and is used to synthesize a MIMO nonlinear fault tolerant control algorithm via MPC. So, we develop a fault detection and isolation (FDI) scheme based on the proposed model. The scheme of the fault detection is applied at every step of MPC control calculation, where we determine the actuator faults and we use it in the MPC optimization problem to determine the new control with respect to the actuator faults. The proposed strategy is tested on numerical simulation and validated on the real system.

Explaining Scales and Statistics of Tropical Precipitation Clusters with a Stochastic Model

AbstractPrecipitation clusters are contiguous raining regions characterized by a precipitation threshold, size, and the total rainfall contained within—termed the cluster power. Tropical observations suggest that the probability distributions of both cluster size and power contain a power-law range (with slope ~ −1.5) bounded by a large-event “cutoff.” Events with values beyond the cutoff signify large, powerful clusters and represent extreme events. A two-dimensional stochastic model is introduced to reproduce the observed cluster distributions, including the slope and the cutoff. The model is equipped with coupled moisture and weak temperature gradient (WTG) energy equations, empirically motivated precipitation parameterization, temporally persistent noise, and lateral mixing processes, all of which collectively shape the model cluster distributions. Moisture–radiative feedbacks aid clustering, but excessively strong feedbacks push the model into a self-aggregating regime. The power-law slope is stable in a realistic parameter range. The cutoff is sensitive to multiple model parameters including the stochastic forcing amplitude, the threshold moisture value that triggers precipitation, and the lateral mixing efficiency. Among the candidates for simple analogs of precipitation clustering, percolation models are ruled out as unsatisfactory, but the stochastic branching process proves useful in formulating a neighbor probability metric. This metric measures the average number of nearest neighbors that a precipitating entity can spawn per time interval and captures the cutoff parameter sensitivity for both cluster size and power. The results here suggest that the clustering tendency and the horizontal scale limiting large tropical precipitating systems arise from aggregate effects of multiple moist processes, which are encapsulated in the neighbor probability metric.

3-D Rotation Representation of Multiple Reflections and Parametric Model for Bistatic Scattering From Arbitrary Multiplate Structure

Based on geometrical optics (GO), this paper develops a representation theory of multiple specular reflections from perfect electric conducting target with an arbitrary patchwise smooth surface. In the course of rays' multiple reflections, the polarization of each ray is found to change in a manner consistent with the variations of its propagating direction, which can be described with an equivalent 3-D rotation transformation. This transformation provides a new representation of the polarization rotation effect induced by multiple reflections. More importantly, the parameters of this transformation characterize the target's GO field response, and their values can classify six classes of geometrical structures. Furthermore, we derive explicit formulas relating these parameters to target's geometrical attributes for double and triple reflections, by using a quaternion algebra method. In order to validate this representation theory, we construct a parametric model of bistatic scattering from an arbitrary multiplate structure, by combining the GO-physical optics (PO) method with this representation. Through two special cases, the representation theory is validated by comparing the RCSs calculated by the parametric model with those by a shooting and bouncing ray (SBR) algorithm. The representation and derived concise parametric model are useful for extracting geometrically relevant features from bistatic radar measurement.

Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders.

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high‐dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.

Parametric model for predicting the reliability of heat power equipment of TPP

Forecasting and ensuring the required high reliability level of heat power plants (thermal power plants) at a stage of their creation and operation is an important problem of economic value. For the solution of this problem in the present article the task is to develop such mathematical models of forecasting of reliability at all stages of life cycle of a thermal power plant from designing before removal from operation. It would be possible to reduce costs of realization of concrete actions. Parametrical mathematical models of growth of reliability have been developed for the solution of an objective. In particular two-parametrical, three-parametrical and multiple parameter models of growth of reliability have been developed and presented. For calculation of estimates of parameters of distribution standard programs are offered. All developed mathematical models of forecasting of reliability level are based on use of the statistical data obtained in the course of tests or operation of products analogs. Change of reliability level on five stages of life cycle of the power equipment of a thermal power plant is graphically shown in the article.

Ex vivo Manufactured Neutrophils for Treatment of Neutropenia-A Process Economic Evaluation.

Neutropenia is a common side-effect of acute myeloid leukemia (AML) chemotherapy characterized by a critical drop in neutrophil blood concentration. Neutropenic patients are prone to infections, experience poorer clinical outcomes, and require expensive medical care. Although transfusions of donor neutrophils are a logical solution to neutropenia, this approach has not gained clinical traction, primarily due to challenges associated with obtaining sufficiently large numbers of neutrophils from donors whilst logistically managing their extremely short shelf-life. A protocol has been developed that produces clinical-scale quantities of neutrophils from hematopoietic stem and progenitor cells (HSPC) in 10 L single-use bioreactors (1). This strategy could be used to mass produce neutrophils and generate sufficient cell numbers to allow decisive clinical trials of neutrophil transfusion. We present a bioprocess model for neutrophil production at relevant clinical-scale. We evaluated two production scenarios, and the impact on cost of goods (COG) of multiple model parameters including cell yield, materials costs, and process duration. The most significant contributors to cost were consumables and raw materials, including the cost of procuring HSPC-containing umbilical cord blood. The model indicates that the most cost-efficient culture volume (batch size) is ~100 L in a single bioreactor. This study serves as a framework for decision-making and optimization strategies when contemplating the production of clinical quantities of cells for allogeneic therapy.

Kuenm: an R package for detailed development of ecological niche models using Maxent.

BackgroundEcological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.ResultsThis package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.DiscussionUse of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.

Fold-Change Detection of NF-κB at Target Genes with Different Transcript Outputs

The transcription factor nuclear factor (NF)-κB promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor (TNF). Although NF-κB signaling exhibits significant variability across single cells, some target genes supporting high levels of TNF-inducible transcription exhibit fold-change detection of NF-κB, which may buffer against stochastic variation in signaling molecules. It is unknown whether fold-change detection is maintained at NF-κB target genes with low levels of TNF-inducible transcription, for which stochastic promoter events may be more pronounced. Here, we used a microfluidic cell-trapping device to measure how TNF-induced activation of NF-κB controls transcription in single Jurkat T cells at the promoters of integrated HIV and the endogenous cytokine gene IL6, which produce only a few transcripts per cell. We tracked TNF-stimulated NF-κB RelA nuclear translocation by live-cell imaging and then quantified transcript number by RNA FISH in the same cell. We found that TNF-induced transcript abundance at 2 h for low- and high-abundance target genes correlates with similar strength with the fold change in nuclear NF-κB. A computational model of TNF-NF-κB signaling, which implements fold-change detection from competition for binding to κB motifs, could reproduce fold-change detection across the experimentally measured range of transcript outputs. However, multiple model parameters affecting transcription had to be simultaneously varied across promoters to maintain fold-change detection while also matching other trends in the single-cell data for low-abundance transcripts. Our results suggest that cells use multiple biological mechanisms to tune transcriptional output while maintaining robustness of NF-κB fold-change detection.

Exploring a mechanistic approach for characterizing transient and steady state foam flow in porous media

Foam model parameters are often derived from laboratory coreflood data at a steady state foam flow regime, which makes them unsuitable for simulating transient foam flow. Since significant foam trapping occurs during transient flow regime, foam trapping process is inadequately covered in most models. In this study, a coreflooding procedure is presented that allows for estimation of multiple foam model parameters in both transient and steady state foam regimes. Surfactant alternating gas (SAG) method of foam injection was modified such that at the end of each SAG cycle, mobile and trapped foams saturations were measured using a coreflood apparatus equipped with in-situ saturation measuring tool. Multiple SAG cycles were then conducted to generate a dataset as a function of water saturation and capillary pressure. The dataset includes trapped and mobile foam saturation, gas mobility reduction factor, flow rate of mobile foam, limiting capillary pressure, critical water saturation below which foam ruptures, and minimum pressure drop to propagate foam across a porous media, all in a single coreflood experiment. This dataset allowed both capillary pressure and initial-residual gas saturation curves to be generated for foam flow in a representative rock sample. Such curves, which are generated for the first time for foam transport, allow the physics of foam flow in the transient and steady state flow to be captured. The behavior of the curves is also dependent on the properties of the foam and porous medium. The possible application of such curves for modelling and planning CO2 sequestration is also discussed. The method is promising for upscaling to field scale in the sense that the test may be conducted in an actual well in the field. More insight on foam transport in porous media as revealed by this method are discussed in details.