Energy Perturbations Research Articles

Ligand and Residue Free Energy Perturbations Solve the Dual Binding Mode Proposal for an A2BAR Partial Agonist.

Adenosine receptors, particularly A2BAR, are gaining attention for their role in pathological conditions such as cancer immunotherapy, prompting the exploration for promising therapeutic applications. Despite numerous selective A2BAR antagonists, the lack of selective full agonists makes the partial agonist BAY60-6583 one of the most interesting activators of this receptor. Recent cryo-EM structures have univocally revealed the binding mode of nonselective ribosidic agonists such as adenosine and its derivative NECA to A2BAR; however, two independent structures with BAY60-6583 show alternative binding orientations, raising the question of which is the physiologically relevant binding mode. In situations such as this, computational simulations that accurately predict shifts in binding free energy can complement experimental structures. Our study combines QligFEP and QresFEP protocols to directly compare the binding affinity of BAY60-6583 between alternative binding modes as well as providing a direct comparison of in silico mutagenesis studies on each pose with experimental mutational effects. Both methods converge on the experimentally determined binding mode that better explains both the existing SAR and mutagenesis data for this ligand. Our results allow the elucidation of the experimental binding orientation that should be considered as a basis for designing partial agonist derivatives with improved affinity and selectivity and underscore the value of free energy perturbation methods in aiding structure-based drug design.

In silico study of some plant compounds as potential anticancer agents targeting MALT1 allosteric domain

Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) is the only human paracaspase, that serves as an adaptor protein and controls substantial genes expressed in the activation, proliferation of lymphocyte, and immune reactions by triggering the IKK/NF-kB signaling pathway. However, unusual MALT1-mediated NF-kB signaling pathway has been identified in multiple diseases like cancer, therefore making MALT1 a promising therapeutic target. There are scanty numbers of MALT1 inhibitors, thus the need to discover more compounds with less or no toxicity issue, that are cheap and pharmacologically efficient is of pertinence. Hence, our present study was to identify phyto-small molecules that could bind the allosteric interface of MALT1 using in silico methods. Total of 34 plant molecules were selected and screened for druglikeness, after which they were docked via Maestro 11.1 against the allosteric site of MALT1. The molecule with a binding score (kcal/mol) better than the control drug was subjected to molecular dynamics (MD) simulations of 100 ns via Desmond, free energy perturbations, principal component and Pearson correlation analyses. Our findings from this computational study presents cyanidin (–8.822 kcal/mol) as better binder to the allosteric site of MALT1 based on the molecular docking and pharmacokinetic profiling than thioridazine. Similarly, cyanidin-MALT1 complex showed significant stability and exhibiting contacts with critical amino acid residues in the site of interest than thioridazine-MALT1 complex. Hence, cyanidin is a potential allosteric inhibitor of MALT1. However, an urgent need for in vitro and in vivo validations is required to ascertain the efficacy of cyanidin in the fight against cancer and other MALT1-related diseases.

Solutions for Tropical Vertical Motion under Convective Quasi-Equilibrium Constraints

Abstract Solutions for tropical convection (vertical motion), including both the first (deep) and the second baroclinic (shallow) modes, subject to convective quasi-equilibrium (CQE) constraints are formulated. Under CQE assumption, tropical convection ω(p, x, y) can be decomposed into a product of height-dependent variable Ωi(p) and space-dependent variable ∇·vi(x, y) with the former constrained by conservation of moist static energy (MSE) or dry static energy (DSE) perturbations, depending on whether the atmospheric column is dominated by ascending or descending motions. We then evaluate the roles of deep and shallow modes of convection in transporting moisture and static energy against observations using the European Centre for Medium-Range Weather Forecasts reanalysis data. The moisture transport by deep mode produces a spatial pattern similar to observations, except for an obvious underestimate of the magnitude over the eastern Pacific convergence zone (EPCZ) and cold tongue areas, where the contribution of shallow mode may account for up to 25% of the total moisture transport. In contrast, the MSE transport by deep mode exhibits a very poor performance, especially over the EPCZ where the observational MSE transport is negative, but a positive value is predicted by deep mode. Including the contribution of shallow mode immediately remedies this deficiency, due to a better representation of the bottom-heavy structure of ascending motions over the EPCZ. These improvements apply to almost the entire tropics, although the correlation tends to decrease away from the convergence zones. Since simple atmospheric models often assume a single heating (forcing) profile to represent the effect of cumulus convection, the present study highlights the importance and feasibility of including both deep and shallow modes in a simple atmospheric model, while at the same time maintaining the simple model framework, to more accurately represent the moisture and MSE transports by convection in the tropics.

Characteristic times for radiation belt drift phase mixing

Impulsive radial transport events occurring in the radiation belts leave lasting marks in the form of drift echoes, that is, energy-dependent drift phase structures in the radiation belts that evolve at the drift frequency. Drift echoes are known to be transient structures that dissipate due to phase mixing. The objective of this paper is to discuss how much time it takes for drift echoes to dissipate, and what drives this phase-mixing process. While any uncertainty or perturbation in the variables controlling trapped particles’ drift frequency contributes to phase mixing, we highlight two main drivers: the observational uncertainty associated with the finite size of the instrument energy channels, and the natural field fluctuations driving perturbations in trapped particles’ drift frequency. It is the combination of both instrumental and natural sources of phase mixing that determines the observed dissipation and lifetime of drift echoes. This means that the observed magnitude and lifetime of a drift echo are always underestimations of the natural magnitude and lifetime of the structure. This calls into question the applicability of the standard, drift-averaged formulation of radial diffusion. The three key points of the study are the following: First, the time it takes for particles initially localized in local time to phase-mix is measured in hours in the Earth’s radiation belts. Second, phase mixing at the drift scale is primarily due to uncertainties in measured kinetic energy and field perturbations. Third, our analysis can be utilized to set an energy resolution requirement for future particle instruments.

Energy Gain Kernel for Climate Feedbacks. Part I: Formulation and Physical Understanding

Abstract This paper introduces a climate feedback kernel, referred to as the “energy gain kernel” (EGK). EGK allows for separating the net longwave radiative energy perturbations given by a Planck feedback matrix explicitly into thermal energy emission perturbations of individual layers and thermal radiative energy flux convergence perturbations at individual layers resulting from the coupled atmosphere–surface temperature changes in response to the unit forcing in individual layers. The former is represented by the diagonal matrix of a Planck feedback matrix and the latter by EGK. Elements of EGK are all positive, representing amplified energy perturbations at a layer where forcing is imposed and energy gained at other layers, both of which are achieved through radiative thermal coupling within an atmosphere–surface column. Applying EGK to input energy perturbations, whether external or internal due to responses of nontemperature feedback processes to external energy perturbations, such as water vapor and albedo feedbacks, yields their total energy perturbations amplified through radiative thermal coupling within an atmosphere–surface column. As the strength of EGK depends exclusively on climate mean states, it offers a solution for effectively and objectively separating control climate state information from climate perturbations for climate feedback studies. Given that an EGK comprises critical climate mean state information on mean temperature, water vapor, clouds, and surface pressure, we envision that the diversity of EGK across different climate models could provide insight into the inquiry of why, under the same anthropogenic greenhouse gas increase scenario, different models yield varying degrees of global mean surface warming. Significance Statement The wide range of 2.5°–4.0°C in global warming projections by climate models hinders our ability to predict its impacts. The newly introduced energy gain kernel (EGK) provides critical information for climate mean infrared opacity, which is collectively determined by climate mean temperature, water vapor, clouds, and surface pressure fields. EGK is directly derived from physical principles without additional definition. EGK captures the temperature feedback’s amplification of energy perturbations initiated from both external forcing and internal nontemperature feedback processes. EGK allows for disentangling positive and negative aspects of temperature feedback, rectifying the common misconception in existing temperature kernels that portray temperature feedback as predominantly negative. The diversity of EGK across different climate models may help explain their varying global warming degrees.

Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows.

Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.

Human Plasma Butyrylcholinesterase Hydrolyzes Atropine: Kinetic and Molecular Modeling Studies.

The participation of butyrylcholinesterase (BChE) in the degradation of atropine has been recurrently addressed for more than 70 years. However, no conclusive answer has been provided for the human enzyme so far. In the present work, a steady-state kinetic analysis performed by spectrophotometry showed that highly purified human plasma BChE tetramer slowly hydrolyzes atropine at pH 7.0 and 25 °C. The affinity of atropine for the enzyme is weak, and the observed kinetic rates versus the atropine concentration was of the first order: the maximum atropine concentration in essays was much less than Km. Thus, the bimolecular rate constant was found to be kcat/Km = 7.7 × 104 M-1 min-1. Rough estimates of catalytic parameters provided slow kcat < 40 min-1 and high Km = 0.3-3.3 mM. Then, using a specific organophosphoryl agent, echothiophate, the time-dependent irreversible inhibition profiles of BChE for hydrolysis of atropine and the standard substrate butyrylthiocholine (BTC) were investigated. This established that both substrates are hydrolyzed at the same site, i.e., S198, as for all substrates of this enzyme. Lastly, molecular docking provided evidence that both atropine isomers bind to the active center of BChE. However, free energy perturbations yielded by the Bennett Acceptance Ratio method suggest that the L-atropine isomer is the most reactive enantiomer. In conclusion, the results provided evidence that plasma BChE slowly hydrolyzes atropine but should have no significant role in its metabolism under current conditions of medical use and even under administration of the highest possible doses of this antimuscarinic drug.

Recent Advances in Electrochemical Detection of Cell Energy Metabolism.

Cell energy metabolism is a complex and multifaceted process by which some of the most important nutrients, particularly glucose and other sugars, are transformed into energy. This complexity is a result of dynamic interactions between multiple components, including ions, metabolic intermediates, and products that arise from biochemical reactions, such as glycolysis and mitochondrial oxidative phosphorylation (OXPHOS), the two main metabolic pathways that provide adenosine triphosphate (ATP), the main source of chemical energy driving various physiological activities. Impaired cell energy metabolism and perturbations or dysfunctions in associated metabolites are frequently implicated in numerous diseases, such as diabetes, cancer, and neurodegenerative and cardiovascular disorders. As a result, altered metabolites hold value as potential disease biomarkers. Electrochemical biosensors are attractive devices for the early diagnosis of many diseases and disorders based on biomarkers due to their advantages of efficiency, simplicity, low cost, high sensitivity, and high selectivity in the detection of anomalies in cellular energy metabolism, including key metabolites involved in glycolysis and mitochondrial processes, such as glucose, lactate, nicotinamide adenine dinucleotide (NADH), reactive oxygen species (ROS), glutamate, and ATP, both in vivo and in vitro. This paper offers a detailed examination of electrochemical biosensors for the detection of glycolytic and mitochondrial metabolites, along with their many applications in cell chips and wearable sensors.

Atomistically informed hierarchical modeling for revisiting the constituent structures from heredity and nano-micro mechanics of sheath-core carbon fiber.

To better understand the heterogeneous anisotropic nanocomposite features and provide reliable underlying constitutive parameters of carbon fiber for continuum-level simulations, hierarchical modeling approaches combining quantum chemistry, molecular dynamics, numerical and analytical micromechanics are employed for studying the structure-performance relationships of the precursor-inherited sheath-core carbon fiber layers. A robust debonding force field is derived from energy matching protocols, including bond dissociation enthalpy calculations and rigid-constraint potential energy surface scan. Logistic long range bond stretching curves with exponential parameters and shifted force vdW curves are designed to diminish energy perturbations. The pseudo-crystalline microstructure is proposed and validated using virtual wide angle X-ray diffraction patterns and bond-orientational order parameters. The distribution or alignment features of the nanocomposite microstructures are collected from quantum chemical topology analysis and normal vector extractions. Non-equilibrium tensile loading simulation predicts the decomposed strain energy contributions, principal-axis modulus, strength limit, localized stress, and fracture morphologies of the model. Finally, an atomistically-informed stiffness prediction model combining numerical homogenization and analytical self-consistent Eshelby-Mori-Tanaka-type effective mean field micromechanics theory is proposed, giving a successful estimation of the overall stiffness matrix of the sheath-core carbon fiber system. The hierarchical models in combination with the carbonization reaction template will help in providing efficient and feasible schemes for the synergistic process-performance control of distinct types of carbon fiber.

PTM-Psi: A python package to facilitate the computational investigation of post-translational modification on protein structures and their impacts on dynamics and functions.

Post-translational modification (PTM) of a protein occurs after it has been synthesized from its genetic template, and involves chemical modifications of the protein's specific amino acid residues. Despite of the central role played by PTM in regulating molecular interactions, particularly those driven by reversible redox reactions, it remains challenging to interpret PTMs in terms of protein dynamics and function because there are numerous combinatorially enormous means for modifying amino acids in response to changes in the protein environment. In this study, we provide a workflow that allows users to interpret how perturbations caused by PTMs affect a protein's properties, dynamics, and interactions with its binding partners based on inferred or experimentally determined protein structure. This Python-based workflow, called PTM-Psi, integrates several established open-source software packages, thereby enabling the user to infer protein structure from sequence, develop force fields for non-standard amino acids using quantum mechanics, calculate free energy perturbations through molecular dynamics simulations, and score the bound complexes via docking algorithms. Using the S-nitrosylation of several cysteines on the GAP2 protein as an example, we demonstrated the utility of PTM-Psi for interpreting sequence-structure-function relationships derived from thiol redox proteomics data. We demonstrate that the S-nitrosylated cysteine that is exposed to the solvent indirectly affects the catalytic reaction of another buried cysteine over a distance in GAP2 protein through the movement of the two ligands. Our workflow tracks the PTMs on residues that are responsive to changes in the redox environment and lays the foundation for the automation of molecular and systems biology modeling.

New MGCAMB tests of gravity with CosmoMC and Cobaya

We present a new version of MGCAMB, a patch for the Einstein-Boltzmann solver CAMB for cosmological tests of gravity. New features include a new cubic-spline parameterization allowing for a simultaneous reconstruction of μ, Σ and the dark energy density fraction Ω X as functions of redshift, the option to work with a direct implementation of μ, Σ (instead of converting to μ, γ first), along with the option to test models with a scalar field coupled only to dark matter, and the option to include dark energy perturbations when working with w ≠ -1 backgrounds, to restore consistency with CAMB in the GR limit. This version of MGCAMB comes with a Python wrapper to run it directly from the Python interface, an implementation in the latest version of CosmoMC, and can be used with Cobaya.

Inhibition of superoxide radical diffusion by Van der Waals forces for boosting photocatalytic H2O2 production

The conversion of superoxide radical (·O2−) to hydrogen peroxide (H2O2) is critical in the photocatalytic formation of H2O2 via the sequential two-step single-electron oxygen reduction mechanism. Providing a high concentration of ·O2− is an effective approach to promoting this process in terms of chemical kinetics. It is, however, extremely challenging to implement this idea in the photocatalytic solution due to the limited lifespan of ·O2−. To this end, a concept about local ·O2− enrichment catalyst surface was proposed in this study and achieved in an innovative PSI/C3N4 photocatalyst. The results of theoretical calculations and temperature-dependent photocatalytic studies indicate that PSI on the C3N4surface can serve as a diffusion buffer for ·O2−, capturing them through Van der Waals interactions and separating them by energy perturbations. As a result, a local enrichment of ·O2− was established on the PSI/C3N4 surface during photocatalysis, strengthening the photocatalytic conversion of ·O2− to H2O2, thus allowing the PSI/C3N4-4 photocatalyst to produce H2O2 at a rate of approximately 2.4 and 2.65 times greater than pure C3N4 under visible light and LED irradiation, respectively. This study presents a promising strategy for enhancing photocatalytic H2O2 production efficiency by selectively enriching ·O2− on the catalyst surface.

Modulation depth of the gyrosynchrotron emission as an identifier of fundamental sausage modes

Aims. We aim to study the intensity, the modulation depth, and the mean modulation depth of the gyrosynchrotron (GS) radiation as a function of the frequency and the line of sight (LOS) in fast sausage modes. Methods. By solving the 2.5D magnetohydrodynamics (MHD) ideal equations of a straight coronal loop considering the chromosphere and with typical flaring plasma parameters we analyse the wavelet transform of the density and the GS emission for different radio frequencies and different spatial resolutions, given impulsive and general perturbations with energies in the microflare range. Results. A wavelet analysis performed over the GS radiation emission showed that a fast fundamental sausage mode of ∼7 s with a first harmonic mode of 3 s developed, for all the initial energy perturbations used. For both the high spatial resolution (central pixel integration) and the low spatial resolution (entire loop integration), the larger the radio frequency, the larger the modulation depth. However, high- and low-resolution integrations differ in that the larger the LOS angle with respect to the loop axis, the larger and smaller the modulation depth, respectively. Conclusions. Fast MHD modes triggered by instantaneous energy depositions of the order of a microflare energy are able to reproduce deep intensity modulation depths in radio emission as observed in solar events. As the trends of the GS emission previously obtained for a linear and forced oscillation remain present when analysing a more general context, considering the chromosphere and where the sausage mode is triggered by an impulsive, non-linear perturbation, it seems that the behaviour found can be used as observational identifiers of the presence of sausage modes with respect to other quasi-periodic pulsation features. It can be inferred from this that finite-amplitude sausage modes have the potential to generate the observed deep modulation depths.

Entropic Dynamics in a Theoretical Framework for Biosystems.

Central to an understanding of the physical nature of biosystems is an apprehension of their ability to control entropy dynamics in their environment. To achieve ongoing stability and survival, living systems must adaptively respond to incoming information signals concerning matter and energy perturbations in their biological continuum (biocontinuum). Entropy dynamics for the living system are then determined by the natural drive for reconciliation of these information divergences in the context of the constraints formed by the geometry of the biocontinuum information space. The configuration of this information geometry is determined by the inherent biological structure, processes and adaptive controls that are necessary for the stable functioning of the organism. The trajectory of this adaptive reconciliation process can be described by an information-theoretic formulation of the living system's procedure for actionable knowledge acquisition that incorporates the axiomatic inference of the Kullback principle of minimum information discrimination (a derivative of Jaynes' principle of maximal entropy). Utilizing relative information for entropic inference provides for the incorporation of a background of the adaptive constraints in biosystems within the operations of Fisher biologic replicator dynamics. This mathematical expression for entropic dynamics within the biocontinuum may then serve as a theoretical framework for the general analysis of biological phenomena.

Methanolic extract of Buchholzia coriacea seed (MEBCS) attenuates lipopolysaccharide-induced perturbations of energy metabolizing enzymes in mice

The safety of lipopolysaccharide (LPS)-containing foods has been questioned and the concerns are understandable. Buchholzia coriacea seeds have been widely applied in folkloric medicine due to their nutritional and therapeutic potential. The aim of the study is to evaluate the attenuation of methanolic extract of Buchholzia coriacea seed (MEBCS) on LPS-induced energy metabolism perturbations. Eighteen BALB/c mice were divided into three groups of 6 mice each. Group 1 (control) received distilled water and physiological saline for 7 days. Group 2 (LPS only) received 4 mg/Kg LPS intraperitoneally on the 7th day of administration, while Group 3 (LPS+MEBCS) were pre-treated with 250 mg/kg MEBCS orally for 6 days prior to administration of LPS (4 mg/kg) on the 7th day. The experimental animals were sacrificed afterward. Glycolytic and oxidative phosphorylation enzymes were assessed in the heart, kidney, spleen, lymphocytes, and erythrocytes. Glycolytic enzymes (lactate dehydrogenase, hexokinase, aldolase, and NADase) and oxidative phosphorylation enzymes such malate dehydrogenase, succinate dehydrogenase and electron transport complexes I+III, II+III, and IV) activities were significantly decreased (P> 0.05) by LPS. Pre-treatment with MEBCS attenuates the decrease in activity observed. The findings of this study indicated that MEBCS has the potential of attenuating perturbations of energy metabolism induced by lipopolysaccharides.

Absolute binding affinities of phospholipids to Erwinia ligand-gated ion channel (ELIC) by streamlined alchemical free energy perturbations (SAFEP).

An increasing number of cryo-EM structures of membrane proteins contain putative lipid binding sites with partially resolved lipid densities. In principle, these densities might be identified by computing the binding free energies of candidate ligands by alchemical free energy perturbation (FEP). In practice, however, these systems have several challenging features including lipid flexibility, system heterogeneity, and low binding affinity. Each of these confounds the distinction between the bound and unbound state: flexible molecules have large accessible volumes in conformation space; heterogeneous environments complicate the unbound state; and open, low-affinity sites mean that a “bound” ligand may still have considerably higher entropy than more in a more traditional, high-affinity binding pocket. Our SAFEP method seeks to address these problems by defining a single collective variable to define the bound state. This distance-from-bound configuration (DBC) metric reliably classifies ligand-protein conformations as “bound” or “unbound.” SAFEP has been successfully applied to the computation of absolute binding affinities of cholesterol to three GPCRs and relative binding affinities of phospholipids to two mutants of ELIC based on structural data. We have extended SAFEP to the calculation of absolute binding affinities of phospholipids. This has required additional sampling of each state and refinement to the collective variables used, especially in the unbound state. While this is somewhat more computationally expensive than computing relative free energies, absolute affinities make functional questions more accessible and mitigate the error propagation inherent to relative binding affinities. It is our hope that these methods will aid in the interpretation of similar systems.

From simulation to application: Elucidating Protoxin-2 binding mechanism in pain channel Nav1.7 through dynamics simulation and biophysics.

Human Nav1.7 is a subtype of voltage-gated sodium channel that initiates and propagates action potentials during pain signaling. It functions by amplifying pain signals during tissue damage, making it an excellent pharmacological target for designing novel analgesics. Protoxin-2 is a known gating modifier toxin that targets the voltage-sensing domain (VSD) in human Nav1.7. However, the difficulty in tuning Protoxin-2 subtype selectivity remains a bottleneck in maximizing its pharmacologic potential. Such complexity in targeting Nav1.7 with Protoxin-2 arises mainly due to the limited knowledge of the biophysical properties and structural basis of toxin-channel interactions. Understanding the Protoxin-2 binding mechanism would aid in engineering a potent toxin that targets Nav1.7 without unwanted physiological effects. It is therefore our goal to map the interaction sites between Protoxin-2 and Nav1.7 VSD that are important for toxin binding. In this study, we are interested in investigating the binding of Protoxin-2 to the voltage-sensing domain of repeat 2 (VSD2) of human Nav1.7. We initially designed mutations of the VSD2 and predicted the dissociation constants of Protoxin-2 using free energy perturbations (FEP) during dynamics simulation. Microscale thermophoresis was employed to validate the FEP results by doing binding assays using the expressed Protoxin-2 and isolated VSD2 in DMPC vesicles. Monte Carlo-based molecular docking was also used to generate and screen mutations that will improve the affinity of Protoxin-2 to Nav1.7. Both the effect and affinity of selected Protoxin-2 mutants will be evaluated with whole-cell electrophysiology. Finally, we want to unravel the role of the membrane in toxin binding by studying the complex formation through solution NMR. Findings from this study would not only shed light on the molecular determinants affecting the toxin binding but also on channel gating function.

Dysfunctional mitochondrial processes contribute to energy perturbations in the brain and neuropsychiatric symptoms.

Mitochondria are complex endosymbionts that evolved from primordial purple nonsulfur bacteria. The incorporation of bacteria-derived mitochondria facilitates a more efficient and effective production of energy than what could be achieved based on previous processes alone. In this case, endosymbiosis has resulted in the seamless coupling of cytochrome c oxidase and F-ATPase to maximize energy production. However, this mechanism also results in the generation of reactive oxygen species (ROS), a phenomenon that can have both positive and negative ramifications on the host. Recent studies have revealed that neuropsychiatric disorders have a pro-inflammatory component in which ROS is capable of initiating damage and cognitive malfunction. Our current understanding of cognition suggests that it is the product of a neuronal network that consumes a substantial amount of energy. Thus, alterations or perturbations of mitochondrial function may alter not only brain energy supply and metabolite generation, but also thought processes and behavior. Mitochondrial abnormalities and oxidative stress have been implicated in several well-known psychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD). As cognition is highly energy-dependent, we propose that the neuronal pathways underlying maladaptive cognitive processing and psychiatric symptoms are most likely dependent on mitochondrial function, and thus involve brain energy translocation and the accumulation of the byproducts of oxidative stress. We also hypothesize that neuropsychiatric symptoms (e.g., disrupted emotional processing) may represent the vestiges of an ancient masked evolutionary response that can be used by both hosts and pathogens to promote self-repair and proliferation via parasitic and/or symbiotic pathways.

Model-form uncertainty quantification of Reynolds-averaged Navier–Stokes modeling of flows over a SD7003 airfoil

Reynolds-averaged Navier–Stokes (RANS) models are known to be inaccurate in complex flows, for instance, laminar-turbulent transition, and RANS uncertainty quantification (UQ) is essential to estimate the uncertainty in their predictions. In this study, a recent physics-based UQ framework that introduces eigenvalue, eigenvector, and turbulence kinetic energy perturbations to the modeled Reynolds stress tensor has been used to estimate the uncertainty in the flow field. We introduce a regression-based marker function that focuses on the turbulence kinetic energy perturbation for the simulation of laminar-turbulent transitional flows over an Selig–Donovan 7003 airfoil. We observed a monotonic behavior of the magnitude of the predicted uncertainty bounds varying with the turbulence kinetic energy perturbation. Importantly, the predicted uncertainty bounds show a synergy behavior that dramatically increases the size of uncertainty bounds and can successfully encompass the reference data when the eigenvalue perturbations are augmented with the marker function.

Modeling ablator grain structure impacts in ICF implosions

High-density carbon is a leading ablator material for inertial confinement fusion (ICF). This and some other ablator materials have grain structure which is believed to introduce very small-scale (∼nm) density inhomogeneity. In principle, such inhomogeneity can affect key ICF metrics like fuel compression and yield, by, for example, acting as a seed for instabilities and inducing mix between ablator and fuel. However, assessments of such effects are uncertain due to the difficulty of modeling this small-scale structure in ICF simulations, typically requiring reduced-resolution modeling that scales these features. We present a grain model and show both the impact of de-resolving grains and the complex mixing dynamics such structures can induce. We find that different methods for de-resolving grains can yield both different total deposition of kinetic energy perturbations and different fuel–ablator mixing. We then show a simple-to-implement approach for approximately conserving the deposition of perturbed kinetic energy and demonstrate that, for the present grain model and test cases, this approach yields a reasonably matched time history of mix width between less and more resolved grain models. The simulations here also demonstrate the complex interaction history between grain-induced mixing and instability around the fuel–ablator interface, showing, for example, that the grain-induced perturbations typically trigger instability of conduction-driven density gradients in the DT fuel, enhancing mix penetration early in the acceleration of the shell. Simulating both microscale and nanoscale grains, we find initial evidence for larger mixing in the microscale case of the present model, despite smaller deposited kinetic energy perturbation.