Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. Results In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. Conclusion Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

Analysis of the Downhole Measurement System’s Pressure and Temperature Measuring Channel Calibration Errors

Various measurements in wells are quite challenging due to the specific measurement conditions. There are some additional requirements for measurement systems, in particular, space restrictions. Therefore, measuring several parameters with a single sensor is rather important. The paper discusses a measurement system that allows measuring temperature and pressure with a single sensor – an SOS-based strain gauge pressure transducer with a bridge or half-bridge circuit. In this case, pressure and temperature measuring channels are calibrated individually, which creates another error component. The numerical simulation of calibration described herein shows that regardless of the sensor circuit, the voltage uncertainty band of both measuring channels is characterized by a reduced error of 0.03 % with a confidence probability P = 0.9.

Probing the spin correlations of $$t{\bar{t}} $$ production at NLO QCD+EW

In this work we investigate the NLO QCD+EW corrections to the top quark pair production and their effects on the spin correlation coefficients and asymmetries at fixed-order top quark pair production and LO decay in the dilepton channel, within the narrow-width approximation. The spin correlations are implicitly measured through the lepton kinematics. Moreover we study the EW effects to the leptonic differential distributions. We find that the EW corrections to the $$t {\bar{t}}$$ t t ¯ production are within the NLO QCD theoretical uncertainties for the spin correlation coefficients and the leptonic asymmetries. On the other hand, for the differential distributions we find that the EW corrections exceed the NLO QCD scale uncertainty band in the high rapidity regimes and are of the order of the NLO QCD scale uncertainty in the case of invariant mass and transverse momentum distributions.

Soft Computing – A Way Ahead to Recover Heat Flux for Short Duration Experiments

The present investigations provide a pathway for implementation of soft computing based Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for prediction of surface heat flux from short duration temperature measurement in shock tubes or shock tunnels. Computational modeling of a co-axial thermal probe is carried out to get the necessary temperature-time histories for different temporal variations of applied heat loads. Different possible inputs are assessed while defining the most suitable ANFIS structure for the recovery of step or ramp heat loads. This proposition is then tested for recovery of heat flux in a given range or of given time history. In each case, the uncertainty band is found to be in the acceptable range. The final assessment of this novel methodology is performed for recovery of heat flux signal from temperature measurement in a shock tube-based experiment. An in-house fabricated fast response coaxial thermal probe (CTP), prepared from chromel (3.25 mm diameter and 10 mm length) and constantan (0.91mm diameter and 15 mm length) is employed for these experiments. The surface heat flux recovered from the experimental signal using ANFIS is seen to have excellent agreement with the conventional analytical method in terms of both trend and magnitude, within an uncertainty band of ± 2%. Therefore present investigations advocate the use of soft computing technique for heat flux recovery in a short duration temperature measurement due to its accuracy of prediction, lesser complexities in mathematical modeling, and being less computationally intensive.

MODELING THE PHASE DIAGRAMS OF THE Tl9SmTe6–Tl4PbTe3 AND Tl9SmTe6–Tl9BiTe6 SYSTEMS

Using the multipurpose genetic algorithm, the analytical models of phase diagrams of the Tl9SmTe6–Tl8Pb2Te6 and Tl9SmTe6–Tl9BiTe6 systems as temperature dependencies of compositions of the equilibrium phases were obtained. The boundaries of the uncertainty band for the liquidus and solidus curves of solid solutions are determined. According to the model of regular solutions of non-molecular compounds, the thermodynamic functions of mixing solid solutions depending on the composition and temperature are determined. It was found that solid solutions based on the Tl9SmTe6, Tl8Pb2Te6 and Tl9BiTe6 compounds are thermodynamically stabile in the whole concentration range

Evolutionary optimization of neural network to predict sediment transport without sedimentation

Sedimentation in open channels occurs frequently and is relative to system inflow. The long-term retention of sediments on channel beds can increase the possibility of variations in deposits and their eventual consolidation. This study compares three hybrid artificial intelligence methods in estimating sediment transport without sedimentation (STWS). We employed the Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) methods in combination with the Artificial Neural Network (ANN) to overcome the weakness of ANN training with conventional algorithms. We used the ICA, GA and PSO methods to optimize the weights of the ANN layers. Using dimensional analysis, we placed the effective parameters in predicting sediment transport into five non-dimensional groups. Six models are proposed and run using three hybrid methods (18 models in total). As the comparisons demonstrate, the proposed combined models are more accurate than ANN and existing equations in estimating the densimetric Froude number (Fr). However, we found the ICA–ANN superior to GA–ANN and PSO–ANN, as it produces explicit solutions to the problem. The ICA–ANN has the lowest prediction uncertainty band for Fr of all developed models. Moreover, the variation trend of the Fr for all input variables (except overall friction factor of sediment) is a second-order polynomial.

Propagating Dam Breach Parametric Uncertainty in a River Reach Using the HEC-RAS Software

Dam break studies consist of two submodels: (a) the dam breach submodel which derives the flood hydrograph and (b) the hydrodynamic submodel which, using the flood hydrograph, derives the flood peaks and maximum water depths in the downstream reaches of the river. In this paper, a thorough investigation of the uncertainty observed in the output of the hydrodynamic model, due to the seven dam breach parameters, is performed in a real-world case study (Papadiana Dam, located at Tavronitis River in Crete, Greece). Three levels of uncertainty are examined (flow peak of the flood hydrograph at the dam location, flow peaks and maximum water depths downstream along the river) with two methods: (a) a Morris-based sensitivity analysis for investigating the influence of each parameter on the final results; (b) a Monte Carlo-based forward uncertainty analysis for defining the distribution of uncertainty band and its statistical characteristics. Among others, it is found that uncertainty of the flow peaks is greater than the uncertainty of the maximum water depths, whereas there is a decreasing trend of uncertainty as we move downstream along the river.

On the impact of non-factorisable corrections in VBF single and double Higgs production

We study the non-factorisable QCD corrections, computed in the eikonal approximation, to Vector-Boson Fusion single and double Higgs production and show the combined factorisable and non-factorisable corrections for both processes at $$ \mathcal{O}\left({\alpha}_s^2\right) $$ O α s 2 . We investigate the validity of the eikonal approximation with and without selection cuts, and carry out an in-depth study of the relative size of the non-factorisable next-to-next-to-leading order corrections compared to the factorisable ones. In the case of single Higgs production, after selection cuts are applied, the non-factorisable corrections are found to be mostly contained within the factorisable scale uncertainty bands. When no cuts are applied, instead, the non-factorisable corrections are slightly outside the scale uncertainty band. Interestingly, for double Higgs production, we find that both before and after applying cuts, non-factorisable corrections are enhanced compared to the single Higgs case. We trace this enhancement to the existence of delicate cancellations between the various leading-order Feynman diagrams, which are partly spoiled by radiative corrections. All contributions studied here have been implemented in proVBFH v1.2.0 and proVBFHH v1.1.0.

NNLO compatibility between pQCD theory and phenomenology in determination of the b-quark pole and MS¯ running masses

This contribution attempts to determine the [Formula: see text]-quark pole mass [Formula: see text] and [Formula: see text] running mass [Formula: see text] with two different approaches at the next-to-next-to-leading order (NNLO) corrections. At the first approach, we derive a relation between the [Formula: see text]-quark pole mass [Formula: see text] and its [Formula: see text] running mass [Formula: see text] at the NNLO corrections based on the perturbative Quantum Chromo Dynamics (pQCD) predictions. At the second approach, we extract numerical values of the [Formula: see text]-quark pole and [Formula: see text] running masses based on the NNLO phenomenology of H1 and ZEUS Collaborations combined beauty vertex production experimental data. Then we discuss about the compatibility between the pQCD theory results and phenomenology approach in determination of the [Formula: see text]-quark pole and [Formula: see text] running masses at the NNLO corrections. Also, we investigate the role and influence of the [Formula: see text]-quark mass as an extra degree of freedom added to the input parameters of the Standard Model Lagrangian, on the improvement of the uncertainty band of the proton parton distribution functions (PDFs) and particularly on the gluon distribution.

In Silico Assessment of 5-FU Therapy via A Mathematical Model with Fuzzy Uncertain Parameters

Background: Ordinary differential equation (ODE) models widely have been used in mathematical oncology to capture dynamics of tumor and immune cells and evaluate the efficacy of treatments. However, for dynamic models of tumor-immune system (TIS), some parameters are uncertain due to inaccurate, missing or incomplete data, which has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of TIS interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. Fuzzy ODE (FODE) models, in comparison with the stochastic differential equation (SDE) models, assigns a fuzzy number instead of a random number (from a specific probability density function) to the parameters, to capture parametric uncertainty. We used FODE model to predict tumor and immune cells dynamics and assess the efficacy of 5-FU. The present model is configurable for 5-FU chemotherapy injection timing and propose testable hypothesis in vitro/ in vivo experiments. Result: FODE model was used to explore the uncertainty of cells dynamics resulting from parametric uncertainty in presence and absence of 5-FU therapy. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and NK cells, and decreasing that of myeloid-derived suppressor (MDSC) cells. We investigate the effect of perturbation on model parameters on dynamics of cells through global sensitivity analysis (GSA) and compute correlation between model parameters and cell dynamics. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient experimental data in the field of mathematical oncology and can predict cells dynamics uncertainty band. In silico assessment of treatments considering parameter uncertainty and investigating the effect of the drugs on movement of cells dynamics uncertainty band may be more appropriate than in crisp setting.