Full Kinetic Model Research Articles

On efficient modelling of radical production in cavitation assisted reactors

Process intensification by cavitation is gaining widespread attention due to the benefits that the intense bubble collapse conditions can provide, yet, several knowledge gaps exist in the modelling of such systems. This work studies the numerical prediction of single bubble dynamics and the various approaches that can be employed to estimate the changes in the chemical composition of cavitating bubbles. Specific emphasis is placed on the prediction of the radical production rates during bubble collapse and the computational performance, with the aim of coupling the single bubble dynamics to flow models for reactor hydrodynamics. The results reveal that the choice of chemical reaction approach has virtually no effect on the bubble dynamics, whereas the predicted radical production rates can differ substantially. It is found that evaluating the radical production only on temperature peaks, an approach commonly followed in literature, may result in the most erroneous estimations (on average 12.8 times larger than those of the full kinetic model), while a simplified kinetic model yields more accurate predictions (2.3 times larger) at the expense of increased computational times. Continuous evaluation of the bubble content by assuming equilibrium when the bubble temperature is above a certain threshold (≈1500K) is shown to be capable of predicting total radical production values close to those estimated by solving the kinetics of a detailed reaction model (19.8% difference), as well as requiring only 22.2% more computational costs compared to simulations without chemical reaction modelling. Such an equilibrium approach is therefore recommended for future studies aiming to couple flow simulations with single bubble dynamics to accurately predict radical production rates in cavitation devices, involving numerous bubbles following different flow trajectories. Furthermore, an algebraic expression that successfully approximates the full kinetic simulation results is proposed as a function of the initial nucleus size and the time integral of the liquid pressure when it is under vapor pressure. Such a model can be applied in modelling efforts that do not require local instantaneous radical concentrations, and paves the way for efficient closure modelling of radical production in CFD simulations of hydrodynamic reactors.

DNA Sequence Control of Enzyme Filamentation and Activation of the SgrAI Endonuclease.

Enzyme polymerization (also known as filamentation) has emerged as a new layer of enzyme regulation. SgrAI is a sequence-dependent DNA endonuclease that forms polymeric filaments with enhanced DNA cleavage activity as well as altered DNA sequence specificity. To better understand this unusual regulatory mechanism, full global kinetic modeling of the reaction pathway, including the enzyme filamentation steps, has been undertaken. Prior work with the primary DNA recognition sequence cleaved by SgrAI has shown how the kinetic rate constants of each reaction step are tuned to maximize activation and DNA cleavage while minimizing the extent of DNA cleavage to the host genome. In the current work, we expand on our prior study by now including DNA cleavage of a secondary recognition sequence, to understand how the sequence of the bound DNA modulates filamentation and activation of SgrAI. The work shows that an allosteric equilibrium between low and high activity states is modulated by the sequence of bound DNA, with primary sequences more prone to activation and filament formation, while SgrAI bound to secondary recognition sequences favor the low (and nonfilamenting) state by up to 40-fold. In addition, the degree of methylation of secondary sequences in the host organism, Streptomyces griseus, is now reported for the first time and shows that as predicted, these sequences are left unprotected from the SgrAI endonuclease making sequence specificity critical in this unusual filament-forming enzyme.

Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay

Abstract Hydrogen-compatible gas turbines are one way to decarbonize electricity production. However, burning and handling hydrogen is not trivial because of its high reactivity and tendency to detonate. Mandatory safety parameters, such as auto-ignition delay times, can be estimated thanks to predictive detailed kinetic models, but with significant calculation times that limit coupling with fluid mechanic codes. An auto-ignition prediction tool was developed based on an artificial intelligence (AI) model for fast computations and an implementation into an explosion model. A dataset of ignition delay times (IDTs) was generated automatically using a recent detailed kinetic model from National University of Galway (NUIG) selected from the literature. Generated data cover a wide operating range and different compositions of fuels. Clustering problems in sample points were avoided by a quasi-random Sobol sequence, which covers uniformly the entire input parameter space. The different algorithms were trained, cross-validated, and tested using a database of more than 70,000 ignitions cases of natural gas/hydrogen blends calculated with the full kinetic model by using a common split of 70/30 for training, testing. The AI model shows a high degree of robustness. For both the training and testing datasets, the average value of the correlation coefficient was above 99.91%, and the mean absolute error (MAE) and the mean square error (MSE) were around 0.03 and lower than 0.04, respectively. Tests showed the robustness of the AI model outside the ranges of pressure, temperature, and equivalence ratio of the dataset. A deterioration is, however, observed with increasing amounts of large alkanes in the natural gas.

Full kinetic modeling analysis of [18F]fluorocholine Positron Emission Tomography (PET) at initial diagnosis of high-grade glioma

PurposeThe main objective was to characterize the tracer uptake kinetics of [18F]fluoromethylcholine ([18F]F-CHO) in high-grade gliomas (HGG) through a full PET kinetic modeling approach. Secondarily, we aimed to explore the relationship between the PET uptake measures and the HGG molecular features. Materials and methodsTwenty-four patients with a suspected diagnosis of HGG were prospectively included. They underwent a dynamic brain [18F]F-CHO-PET/CT, from which a tumoral time-activity curve was extracted. The plasma input function was obtained through arterial blood sampling with metabolite correction. These data were fitted to 1- and 2-tissue-compartment models, the best of which was selected through the Akaike information criterion. We assessed the correlation between the kinetic parameters and the conventional static PET metrics (SUVmax, SUVmean and tumor-to-background ratio TBR). We explored the association between the [18F]F-CHO-PET quantitative parameters and relevant molecular biomarkers in HGG. ResultsTumoral time-activity curves in all patients showed a rapid rise of [18F]F-CHO uptake followed by a plateau-like shape. Best fits were obtained with near-irreversible 2-tissue-compartment models. The perfusion-transport constant K1 and the net influx rate Ki showed strong correlation with SUVmax (r = 0.808–0.861), SUVmean (r = 0.794–0.851) and TBR (r = 0.643–0.784), p < 0.002. HGG was confirmed in 21 patients, of which those with methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter showed higher mean Ki (p = 0.020), K1 (p = 0.025) and TBR (p = 0.001) than the unmethylated ones. Conclusion[18F]F-CHO uptake kinetics in HGG is best explained by a 2-tissue-compartment model. The conventional static [18F]F-CHO-PET measures have been validated against the perfusion-transport constant (K1) and the net influx rate (Ki) derived from kinetic modeling. A relationship between [18F]F-CHO uptake rate and MGMT methylation is suggested but needs further confirmation.

Update on [18F]ACI‐12589: a novel and promising PET tracer for a‐synuclein

AbstractBackgroundParkinson’s disease (PD) and other synucleinopathies, such as Multiple System Atrophy (MSA), involve a progressive accumulation of a‐synuclein (a‐syn) inclusions which correlate well with clinical manifestations. A PET agent capable of imaging specifically and selectively a‐syn inclusions could significantly improve differential diagnosis and clinical trials for drug candidates targeting a‐syn. Here, we report the preclinical and initial clinical characterization of [18F]ACI‐12589 in patients with different synucleinopathies.MethodLeveraging AC Immune’s proprietary small molecule Morphomer® library, compounds were optimized for affinity and specificity for pathological a‐syn as well as for selectivity versus frequent co‐pathologies, using autoradiography and radiobinding techniques. Based on these results [18F]ACI‐12589, was selected for initial clinical evaluation. PET scans were acquired for approximately 50 participants including idiopathic and genetic PD, MSA, Dementia with Lewy bodies (DLB) in addition to controls and cases from other neurodegenerative disorders (NDDs). Initial participants underwent 0‐90 min dynamic scans with arterial blood sampling allowing full kinetic modeling, while following scans were acquired as 60‐90 min static scans.ResultWhen tested on tissue sections, ACI‐12589 demonstrated a specific signal matching the distribution of pathological a‐syn. This target engagement was observed across a range of cases with different synucleinopathy diagnoses, but also in tissues from other NDDs, when a‐syn inclusions were present as co‐pathology. ACI‐12589 is selective in vitro versus frequent co‐pathologies such as amyloid‐b and Tau and has a clean off‐target profile, including for MAO‐A and B. Initial clinical data indicate that [18F]ACI‐12589 displayed substantial retention in MSA cases in expected disease‐affected brain areas based on clinical manifestations. When quantified, the signal in the cerebellar peduncles was clearly discriminating the MSA group from controls, synucleinopathy cases and other NDDs. In addition, this signal could not be blocked in vivo by the MAO‐B inhibitor Selegiline. Although to a lower level than in MSA cases, some signal retention was observed in different NDDs.Conclusion[18F]ACI‐12589 displays the preclinical and clinical characteristics deemed necessary to become a reliable and accurate PET radiotracer for the detection of a‐syn pathology in MSA. Additional evaluations of this tracer will focus on the longitudinal and early disease stage assessments.

Using a Quench Level Approximation to Estimate the Effect of Metallicity on the Abundances of N-bearing Species in H2-dominated Atmospheres

Variations in atmospheric elemental nitrogen can considerably affect the abundance of major nitrogen-bearing species such as NH3 and HCN. Also, due to vertical mixing and photochemistry, their abundance deviates from thermochemical equilibrium. The goal of this study is to understand the effect of atmospheric metallicity on the composition of NH3, N2, and HCN over a large parameter space in the presence of vertical mixing, which when combined with the work on CHO-bearing species in Soni & Acharyya can provide a comprehensive understanding of the effect of atmospheric metallicity. We used quenching approximations and a full chemical kinetics model for the calculations, and a comparison between these two methods was made. To generate thermal profiles, the petitRADTRANS code was used. Chemical timescales of NH3 and N2 are found to be complex functions of metallicity, while HCN is inversely proportional. Using quenched abundances of NH3 and CO, the quenched abundance of HCN can be constrained since it remains in equilibrium with NH3, CO, and H2O. Quenched NH3 increases with increasing K zz until a particular point, after which it becomes independent of vertical mixing. There is a sweet spot in the K zz parameter space to maximize the quenched HCN for a given T int and T equi; the parameter space moves toward a lower equilibrium temperature, and the abundance of HCN increases with metallicity. Finally, we used a data set of quenched abundances to provide a list of potential candidates in which the observation of HCN is possible.

The deposition kinetics of barium sulphate scale: model development

The deposition kinetics of barium sulphate scale: model development

A combined immersed finite element and conservative semi-Lagrangian scheme for plasma-material interactions

In this paper, the immersed finite element (IFE) coupled with the conservative semi-Lagrangian (CSL) kinetic scheme is developed for plasma-material interactions. The proposed method (IFE-CSL) enjoys respective advantages of the IFE and CSL, i.e., the flexibility and efficiency for treating complex boundary conditions, mass conservation and being free from the Courant-Friedrichs-Lewy (CFL) condition. In the current IFE-CSL, the IFE method based on structured interface-independent meshes is developed for the spatial discretization of the field equation, which provides an accurate approach with convenient implementations to solve the field problems with irregular interface. The CSL scheme combined with an immersed method termed half-way ghost-cell is proposed for the spatial discretization of the Vlasov equation, which enables the proposed method to exactly conserve mass and conveniently handle curved boundaries. Then the IFE and CSL method are coupled via the charge density depending on full kinetic or hybrid kinetic model, where the appropriate IFE solver is developed for the linear or nonlinear Poisson equation, respectively. In the current IFE-CSL, different geometries can be treated automatically for both IFE and CSL through the specific geometric information. As a result, the proposed CSL-IFE can be conveniently and efficiently applied to simulate the plasma-material interactions with structured interface-independent meshes. Finally, several numerical experiments are performed to demonstrate the capabilities of the proposed method.

A kinetic study of biotransformation from valencene to nootkatone in an oscillatory baffled reactor.

In this paper we report our kinetic study of an oxidation reaction from valencene to nootkatone using enzyme in an oscillatory baffled reactor. The aims of this work are to elucidate the reaction mechanism and evaluate reaction kinetics. Towards these objectives, a full kinetic model using the Langmuir-Hinshelwood method was established and applied to the experimental data, allowing reactor schemes and orders to be confirmed and reaction rate constants to be extracted. Our full kinetic analysis suggests that most of the reversible reaction steps can be treated as irreversible, simplifying the overall reaction schemes. The effect of mass transfer on the kinetics was also investigated. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Evaluation of [18F]PF-06455943 as a Potential LRRK2 PET Imaging Agent in the Brain of Nonhuman Primates.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the common causes of inherited Parkinson's disease (PD) and emerged as a causative PD gene. Particularly, LRRK2-Gly2019Ser mutation was reported to alter the early phase of neuronal differentiation, increasing cell death. Selective inhibitors of LRRK2 kinase activity were considered as a promising therapeutic target for PD treatment. However, the development of effective brain-penetrant LRRK2 inhibitors remains challenging. Recently, we have developed a novel positron emission tomography (PET) radioligand for LRRK2 imaging and demonstrated preferable tracer properties in rodents. Herein, we evaluate [18F]PF-06455943 quantification methods in the nonhuman primate (NHP) brain using full kinetic modeling with radiometabolite-corrected arterial blood samples, and homologous blocking with two doses (0.1 and 0.3 mg/kg). Kinetic analysis results demonstrated that a two-tissue compartmental model and a Logan graphical analysis are appropriate for [18F]PF-06455943 PET quantification. In addition, we observed that total distribution volume (VT) values can be reliably estimated with as short as a 30 min scan duration. Homologous blocking studies confirmed the specific binding of [18F]PF-06455943 and revealed that the nonradioactive mass of PF-06455943 achieved 45-55% of VT displacement in the whole brain. This work supports the translation of [18F]PF-06455943 PET imaging for the human brain and target occupancy studies.

The role of filamentation in activation and DNA sequence specificity of the sequence-specific endonuclease SgrAI.

Filament formation by metabolic, biosynthetic, and other enzymes has recently come into focus as a mechanism to fine-tune enzyme activity in the cell. Filamentation is key to the function of SgrAI, a sequence-specific DNA endonuclease that has served as a model system to provide some of the deepest insights into the biophysical characteristics of filamentation and its functional consequences. Structure-function analyses reveal that, in the filamentous state, SgrAI stabilizes an activated enzyme conformation that leads to accelerated DNA cleavage activity and expanded DNA sequence specificity. The latter is thought to be mediated by sequence-specific DNA structure, protein-DNA interactions, and a disorder-to-order transition in the protein, which collectively affect the relative stabilities of the inactive, non-filamentous conformation and the active, filamentous conformation of SgrAI bound to DNA. Full global kinetic modeling of the DNA cleavage pathway reveals a slow, rate-limiting, second-order association rate constant for filament assembly, and simulations of in vivo activity predict that filamentation is superior to non-filamenting mechanisms in ensuring rapid activation and sequestration of SgrAI's DNA cleavage activity on phage DNA and away from the host chromosome. In vivo studies demonstrate the critical requirement for accelerated DNA cleavage by SgrAI in its biological role to safeguard the bacterial host. Collectively, these data have advanced our understanding of how filamentation can regulate enzyme structure and function, while the experimental strategies used for SgrAI can be applied to other enzymatic systems to identify novel functional roles for filamentation.

Ballistic-to-hydrodynamic transition and collective modes for two-dimensional electron systems in magnetic field

The recent demonstrations of viscous hydrodynamic electron flow in two-dimensional electron systems poses serious questions to the validity of existing transport theories, including the ballistic model, the collision-induced and collisionless hydrodynamics. While the theories of transport at hydrodynamic-to-ballistic crossover for free 2d electrons are well established, the same is not true for electrons in magnetic fields. In this work, we develop an analytically solvable model describing the transition from ballistic to hydrodynamic transport with changing the strength of electron-electron collisions in magnetic fields. Within this model, we find an expression for the high-frequency non-local conductivity tensor of 2d electrons. It is valid at arbitrary relation between frequency of external field $\omega$, the cyclotron frequency $\omega_c$, and the frequency of e-e collisions $\tau^{-1}_{ee}$. We use the obtained expression to study the transformation of 2d magnetoplasmon modes at hydrodynamic-to-ballistic crossover. In the true hydrodynamic regime, $\omega\tau_{ee} \ll 1$, the 2DES supports a single magnetoplasmon mode that is not split at cyclotron harmonics. In the ballistic regime, $\omega\tau_{ee} \gg 1$, the plasmon dispersion develops splittings at cyclotron harmonics, forming the Bernstein modes. A formal long-wavelength expansion of kinetic equations ("collisionless hydrodynamics") predicts the first splitting of plasmon dispersion at $\omega\approx 2\omega_c$. Still, such expansion fails to predict the zero and negative group velocity sections of true magnetoplasmon dispersion, for which the full kinetic model is required.

A mini-chemical scheme with net reactions for 3D general circulation models

Context. Growing evidence has indicated that the global composition distribution plays an indisputable role in interpreting observational data. Three-dimensional general circulation models (GCMs) with a reliable treatment of chemistry and clouds are particularly crucial in preparing for upcoming observations. In attempts to achieve 3D chemistry-climate modeling, the challenge mainly lies in the expensive computing power required for treating a large number of chemical species and reactions. Aims. Motivated by the need for a robust and computationally efficient chemical scheme, we devise a mini-chemical network with a minimal number of species and reactions for H2-dominated atmospheres. Methods. We apply a novel technique to simplify the chemical network from a full kinetics model, VULCAN, by replacing a large number of intermediate reactions with net reactions. The number of chemical species is cut down from 67 to 12, with the major species of thermal and observational importance retained, including H2O, CH4, CO, CO2, C2H2, NH3, and HCN. The size of the total reactions is also greatly reduced, from ~800 to 20. We validated the mini-chemical scheme by verifying the temporal evolution and benchmarking the predicted compositions in four exoplanet atmospheres (GJ 1214b, GJ 436b, HD 189733b, and HD 209458b) against the full kinetics of VULCAN. Results. The mini-network reproduces the chemical timescales and composition distributions of the full kinetics well within an order of magnitude for the major species in the pressure range of 1 bar–0.1 mbar across various metallicities and carbon-to-oxygen (C/O) ratios. Conclusions. We have developed and validated a mini-chemical scheme using net reactions to significantly simplify a large chemical network. The small scale of the mini-chemical scheme permits simple use and fast computation, which is optimal for implementation in a 3D GCM or a retrieval framework. We focus on the thermochemical kinetics of net reactions in this paper and address photochemistry in a follow-up paper.

Feasibility study of PET dynamic imaging of [18F]DHMT for quantification of reactive oxygen species in the myocardium of large animals

ObjectivesWe aimed to develop a dynamic imaging technique for a novel PET superoxide tracer, [18F]DHMT, to allow for absolute quantification of myocardial reactive oxygen species (ROS) production in a large animal model. MethodsSix beagle dogs underwent a single baseline dynamic [18F]DHMT PET study, whereas one animal underwent three serial dynamic studies over the course of chronic doxorubicin administration (1 mg·kg−1·week−1 for 15 weeks). During the scans, sequential arterial blood samples were obtained for plasma metabolite correction. The optimal compartment model and graphical analysis method were identified for kinetic modeling. Values for the left ventricular (LV) net influx rate, Ki, were reported for all the studies and compared with the LV standard uptake values (SUVs) and the LV-to-blood pool SUV ratios from the 60 to 90 minute static images. Parametric images were also generated. Results[18F]DHMT followed irreversible kinetics once oxidized within the myocardium in the presence of superoxide, as evidenced by the fitting generated by the irreversible two-tissue (2Ti) compartment model and the linearity of Patlak analysis. Myocardial Ki values showed a weak correlation with LV SUV (R2 = 0.27), but a strong correlation with LV-to-blood pool SUV ratio (R2 = 0.92). Generation of high-quality parametric images showed superior myocardial to blood contrast compared to static images. ConclusionsA dynamic PET imaging technique for [18F]DHMT was developed with full and simplified kinetic modeling for absolute quantification of myocardial superoxide production in a large animal model.

Comparison of high-order Eulerian methods for electron hybrid model

In this work, we focus on the numerical approximation of a hybrid fluid-kinetic plasma model for electrons, in which energetic electrons are described by a Vlasov kinetic model whereas a fluid model is used for the cold population of electrons. First, we study the validity of this hybrid modeling in a two dimensional context (one dimension in space and one dimension in velocity) against the full (stiff) Vlasov kinetic model and second, a four dimensional configuration is considered (one dimension in space and three dimensions in velocity) following [1]. To do so, we consider two numerical Eulerian methods. The first one is based on the Hamiltonian structure of the hybrid system and the second approach, which is based on exponential integrators, enables to derive high order integrator and remove the CFL condition induced by the linear part. The efficiency of these methods, which are combined with an adaptive time stepping strategy, is discussed in the different configurations and in the linear and nonlinear regimes.

Bridging hybrid- and full-kinetic models with Landau-fluid electrons

Context. Magnetic reconnection plays a fundamental role in plasma dynamics under many different conditions, from space and astrophysical environments to laboratory devices. High-resolution in situ measurements from space missions allow naturally occurring reconnection processes to be studied in great detail. Alongside direct measurements, numerical simulations play a key role in the investigation of the fundamental physics underlying magnetic reconnection, also providing a testing ground for current models and theory. The choice of an adequate plasma model to be employed in numerical simulations, while also compromising with computational cost, is crucial for efficiently addressing the problem under study. Aims. We consider a new plasma model that includes a refined electron response within the “hybrid-kinetic framework” (fully kinetic protons and fluid electrons). The extent to which this new model can reproduce a full-kinetic description of 2D reconnection, with particular focus on its robustness during the nonlinear stage, is evaluated. Methods. We perform 2D simulations of magnetic reconnection with moderate guide field by means of three different plasma models: (i) a hybrid-Vlasov-Maxwell model with isotropic, isothermal electrons, (ii) a hybrid-Vlasov-Landau-fluid (HVLF) model where an anisotropic electron fluid is equipped with a Landau-fluid closure, and (iii) a full-kinetic model. Results. When compared to the full-kinetic case, the HVLF model effectively reproduces the main features of magnetic reconnection, as well as several aspects of the associated electron microphysics and its feedback onto proton dynamics. This includes the global evolution of magnetic reconnection and the local physics occurring within the so-called electron-diffusion region, as well as the evolution of species’ pressure anisotropy. In particular, anisotropy-driven instabilities (such as fire-hose, mirror, and cyclotron instabilities) play a relevant role in regulating electrons’ anisotropy during the nonlinear stage of magnetic reconnection. As expected, the HVLF model captures all these features, except for the electron-cyclotron instability.

Isogeometric schemes in rarefied gas dynamics context

Geometrical complexity is typical of missile and reentry systems, small-scale devices with intrinsic milli-to-micro scale features, or in general topology-rich systems. Additionally, at high altitudes or at microscales, when the flow becomes rarefied, the conventional continuum/macroscopic equations produce unsatisfactory results. A microscopic/kinetic description becomes necessary. In this work, we introduce isogeometric (IG) kinetic schemes (KS) for the full Boltzmann equation and related kinetic models (Bhatnagar–Gross–Krook (BGK), Ellipsoidal statistical BGK, and Shakov). These schemes are specifically aimed for modeling flows in topologically complex “multi-patch” geometries such as those constructed using computer-aided geometric design tools; high order accurate; unconditionally time-stable (as per our tests) following generalized-α method; non-linearly stable following streamlined upwind Petrov–Galerkin method applied at every patch level with patch-to-patch coupling enforced either strongly by constraining the interface using single degree of freedom, or weakly using numerical fluxes as in discontinuous Galerkin (useful because the underlying system is first order in space); amenable to nearly-linear parallel efficiency owing to the six-dimensional nature of Boltzmann equation, wherein the convection and collision can be, respectively, performed simultaneously over velocity and physical spaces. In particular, since the surface representation is exact in IG, when surface properties are to be computed, which is often the case in aerodynamic applications, these schemes perform well. A series of verification tests for 1D/2D/3D-3V initial–boundary value and boundary-value flow problems, involving momentum, energy, and diffusive transport, including problems posed on non-conforming domains/patches, is performed to illustrate the stability and accuracy of the proposed method.

Fully Integrated Quantitative Multiparametric Analysis of Non-Small Cell Lung Cancer at 3-T PET/MRI: Toward One-Stop-Shop Tumor Biological Characterization at the Supervoxel Level.

The aim of this study was to study the feasibility of a fully integrated multiparametric imaging framework to characterize non-small cell lung cancer (NSCLC) at 3-T PET/MRI. An 18F-FDG PET/MRI multiparametric imaging framework was developed and prospectively applied to 11 biopsy-proven NSCLC patients. For each tumor, 12 parametric maps were generated, including PET full kinetic modeling, apparent diffusion coefficient, T1/T2 relaxation times, and DCE full kinetic modeling. Gaussian mixture model-based clustering was applied at the whole data set level to define supervoxels of similar multidimensional PET/MRI behaviors. Taking the multidimensional voxel behaviors as input and the supervoxel class as output, machine learning procedure was finally trained and validated voxelwise to reveal the dominant PET/MRI characteristics of these supervoxels at the whole data set and individual tumor levels. The Gaussian mixture model-based clustering clustering applied at the whole data set level (17,316 voxels) found 3 main multidimensional behaviors underpinned by the 12 PET/MRI quantitative parameters. Four dominant PET/MRI parameters of clinical relevance (PET: k2, k3 and DCE: ve, vp) predicted the overall supervoxel behavior with 97% of accuracy (SD, 0.7; 10-fold cross-validation). At the individual tumor level, these dimensionality-reduced supervoxel maps showed mean discrepancy of 16.7% compared with the original ones. One-stop-shop PET/MRI multiparametric quantitative analysis of NSCLC is clinically feasible. Both PET and MRI parameters are useful to characterize the behavior of tumors at the supervoxel level. In the era of precision medicine, the full capabilities of PET/MRI would give further insight of the characterization of NSCLC behavior, opening new avenues toward image-based personalized medicine in this field.

PET Molecular Imaging: A Holistic Review of Current Practice and Emerging Perspectives for Diagnosis, Therapeutic Evaluation and Prognosis in Clinical Oncology.

PET/CT molecular imaging has been imposed in clinical oncological practice over the past 20 years, driven by its two well-grounded foundations: quantification and radiolabeled molecular probe vectorization. From basic visual interpretation to more sophisticated full kinetic modeling, PET technology provides a unique opportunity to characterize various biological processes with different levels of analysis. In clinical practice, many efforts have been made during the last two decades to standardize image analyses at the international level, but advanced metrics are still under use in practice. In parallel, the integration of PET imaging with radionuclide therapy, also known as radiolabeled theranostics, has paved the way towards highly sensitive radionuclide-based precision medicine, with major breakthroughs emerging in neuroendocrine tumors and prostate cancer. PET imaging of tumor immunity and beyond is also emerging, emphasizing the unique capabilities of PET molecular imaging to constantly adapt to emerging oncological challenges. However, these new horizons face the growing complexity of multidimensional data. In the era of precision medicine, statistical and computer sciences are currently revolutionizing image-based decision making, paving the way for more holistic cancer molecular imaging analyses at the whole-body level.

Kinetic model of the COMPASS tokamak SOL

Abstract In this work we report on results of full size kinetic modelling of the COMPASS tokamak SOL. Presented simulations indicate, that i. kinetic effects are significant in the inner divertor (ID) plasma; ii. normalized power loads to the ID are above the classical values and are caused by non-Maxwellian super-thermal electrons; iii. different divertor current regimes do not influence overall SOL parameters, except those of the divertor sheath; iv. ions at the divertors plasma are colder than the electrons; v. parallel heat transport is strongly non-local. Modelling results are in a reasonable agreement with the experimental measurements.