Scattering spectra models for physics.

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.

Topological wetting states of microdroplets on closed-loop structured surfaces: Breakdown of the Gibbs equation at the microscale.

Microdroplets are a class of soft matter that has been extensively employed for chemical, biochemical, and industrial applications. However, fabricating microdroplets with largely controllable contact-area shape and apparent contact angle, a key prerequisite for their applications, is still a challenge. Here, by engineering a type of surface with homocentric closed-loop microwalls/microchannels, we can achieve facile size, shape, and contact-angle tunability of microdroplets on the textured surfaces by design. More importantly, this class of surface topologies (with universal genus value = 1) allows us to reveal that the conventional Gibbs equation (widely used for assessing the edge effect on the apparent contact angle of macrodroplets) seems no longer applicable for water microdroplets or nanodroplets (evidenced by independent molecular dynamics simulations). Notably, for the flat surface with the intrinsic contact angle ~0°, we find that the critical contact angle on the microtextured counterparts (at edge angle 90°) can be as large as >130°, rather than 90° according to the Gibbs equation. Experiments show that the breakdown of the Gibbs equation occurs for microdroplets of different types of liquids including alcohol and hydrocarbon oils. Overall, the microtextured surface design and topological wetting states not only offer opportunities for diverse applications of microdroplets such as controllable chemical reactions and low-cost circuit fabrications but also provide testbeds for advancing the fundamental surface science of wetting beyond the Gibbs equation.

Bayesian modeling of spatially differentiated multivariate enamel defects of the children's primary maxillary central incisor teeth.

BACKGROUND: The analysis of dental caries has been a major focus of recent work on modeling dental defect data. While a dental caries focus is of major importance in dental research, the examination of developmental defects which could also contribute at an early stage of dental caries formation, is also of potential interest. This paper proposes a set of methods which address the appearance of different combinations of defects across different tooth regions. In our modeling we assess the linkages between tooth region development and both the type of defect and associations with etiological predictors of the defects which could be influential at different times during the tooth crown development. METHODS: We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application. RESULTS: The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results. CONCLUSION: We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model.

A new insight into the surface adsorption in the solution phase: A modification of the Langmuir isotherm.

The Langmuir isotherm, originally developed to study the adsorption of gases, has been modified in this research to investigate the adsorption of solutes in the solution phase. The modification considers the adsorption of solvent molecules and the interactions between adsorbed particles and the species in the solution. Three equations have been obtained to calculate the contribution of these additional effects on the Gibbs free energy, enthalpy, and entropy of solute adsorption based on the new isotherm. The study evaluated the efficiency of the new isotherm in the adsorption of some metal ions in an aqueous solution and found that it is more accurate than the Langmuir isotherm and provides a deeper insight into the adsorption process in the solution phase. PRACTITIONER POINTS: Modification of the Langmuir isotherm for adsorption in solution. Comparison of the efficiency of the Langmuir and modified Langmuir isotherms. Accurate determination of ∆ H ads o $$ \Delta {H}_{ads}^o $$ and ∆ S ads o $$ \Delta {S}_{ads}^o $$ for Pb(II), Cd(II), and Ni(II) adsorption.

Multilevel Bayesian network to model child morbidity using Gibbs sampling.

Bayesian networks (BNs) are suitable models for studying complex interdependencies between multiple health outcomes, simultaneously. However, these models fail the assumption of independent observation in the case of hierarchical data. Therefore, this study proposes a two and three-level random intercept multilevel Bayesian network (MBN) models to study the conditional dependencies between multiple outcomes. The structure of MBN was learned using the connected three parent set block Gibbs sampler, where each local network was included based on Bayesian information criteria (BIC) score of multilevel regression. These models were examined using simulated data assuming features of both multilevel models and BNs. The estimated area under the receiver operating characteristics for both models were above 0.8, indicating good fit. The MBN was then applied to real child morbidity data from the 2016 Ethiopian Demographic Health Survey (EDHS). The result shows a complex causal dependencies between malnutrition indicators and child morbidities such as anemia, acute respiratory infection (ARI) and diarrhea. According to this result, families and health professionals should give special attention to children who suffer from malnutrition and also have one of these illnesses, as the co-occurrence of both can worsen the health of a child.

Thermodynamic Analysis to Evaluate the Effect of Diet on Brain Glucose Metabolism: The Case of Fish Oil.

Inefficient glucose metabolism and decreased ATP production in the brain are linked to ageing, cognitive decline, and neurodegenerative diseases (NDDs). This study employed thermodynamic analysis to assess the effect of fish oil supplementation on glucose metabolism in ageing brains. Data from previous studies on glucose metabolism in the aged human brain and grey mouse lemur brains were examined. The results demonstrated that Omega-3 fish oil supplementation in grey mouse lemurs increased entropy generation and decreased Gibbs free energy across all brain regions. Specifically, there was a 47.4% increase in entropy generation and a 47.4 decrease in Gibbs free energy in the whole brain, indicating improved metabolic efficiency. In the human model, looking at the specific brain regions, supplementation with Omega-3 polyunsaturated fatty acids (n-3 PUFAs) reduced the entropy generation difference between elderly and young individuals in the cerebellum and particular parts of the brain cortex, namely the anterior cingulate and occipital lobe, with 100%, 14.29%, and 20% reductions, respectively. The Gibbs free energy difference was reduced only in the anterior cingulate by 60.64%. This research underscores that the application of thermodynamics is a comparable and powerful tool in comprehending the dynamics and metabolic intricacies within the brain.

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data.

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

Theoretical investigation on the solid-liquid phase transition of gallium through free energy analysis.

CONTEXT: Gallium, renowned for its notably low melting point and unique property of becoming liquid at room temperature, is a valuable constituent in phase change materials. In this study, we investigate the solid-liquid phase transition of gallium using the modified embedded atom method (MEAM) potential. It addresses the technique to compute the free energy difference between the solid and liquid without using a reference state. We examine various thermodynamic and dynamic properties, including density, specific heat capacity, diffusivity, and radial distribution functions. We compute the coexistence temperature of the solid-liquid phase transitions of gallium from free energy analysis. This information is crucial for understanding the behavior of the material under different pressure conditions and can be valuable for various applications, such as materials processing and high-pressure studies. The analysis, findings, and insights of the present work will be of great significance to the broad scientific and engineering communities in the field of phase transformation of materials. METHODS: A series of molecular dynamics(MD) simulations were conducted using the LAMMPS software packages. The gallium atoms are modeled using the modified embedded atom method (MEAM) potential. To accurately predict the solid-liquid phase transitions of gallium, we calculated free energy by employing the "constrained λ integration" method, coupled with multiple histogram reweighting (MHR). The solid-liquid coexistence line is determined through the Gibbs-Duhem integration technique.

Magnetic Black Hole Thermodynamics in an Extended Phase Space with Nonlinear Electrodynamics.

We study Einstein's gravity coupled to nonlinear electrodynamics with two parameters in anti-de Sitter spacetime. Magnetically charged black holes in an extended phase space are investigated. We obtain the mass and metric functions and the asymptotic and corrections to the Reissner-Nordström metric function when the cosmological constant vanishes. The first law of black hole thermodynamics in an extended phase space is formulated and the magnetic potential and the thermodynamic conjugate to the coupling are obtained. We prove the generalized Smarr relation. The heat capacity and the Gibbs free energy are computed and the phase transitions are studied. It is shown that the electric fields of charged objects at the origin and the electrostatic self-energy are finite within the nonlinear electrodynamics proposed.

The minimum energy required to build a cell.

Understanding the energy requirements for cell synthesis accurately and comprehensively has been a longstanding challenge. We introduce a computational model that estimates the minimum energy necessary to build any cell from its constituent parts. This method combines omics and internal cell compositions from various sources to calculate the Gibbs Free Energy of biosynthesis independently of specific metabolic pathways. Our public tool, Synercell, can be used with other models for minumum species-specific energy estimations in any well-sequenced species. The energy for synthesising the genome, transcriptome, proteome, and lipid bilayer of four cell types: Escherichia coli, Saccharomyces cerevisiae, an average mammalian cell and JCVI-syn3A were estimated. Their modelled minimum synthesis energies at 298 K were 9.54 × 10 - 11 J/cell, 4.99 × 10 - 9 J/cell, 3.71 × 10 - 7 J/cell and 3.69 × 10 - 12 respectively. Gram-for-gram synthesis of lipid bilayers requires the most energy, followed by the proteome, genome, and transcriptome. The average per gram cost of biomass synthesis is in the 300s of J/g for all four cells. Implications for the generalisability of cell construction and applications to biogeosciences, cellular biology, biotechnology, and astrobiology are discussed.

Modeled Pathways and Fluxes of PCB Dechlorination by Redox Potentials.

Dechlorination is one of the main processes for the natural degradation of polychlorinated biphenyls (PCBs) in an anaerobic environment. However, PCB dechlorination pathways and products vary with PCB congeners, types of functional dechlorinating bacteria, and environmental conditions. The present study develops a novel model for determining dechlorination pathways and fluxes by tracking redox potential variability, transforming the complex dechlorination process into a stepwise sequence. The redox potential is calculated via the Gibbs free energy of formation, PCB concentrations in reactants and products, and environmental conditions. Thus, the continuous change in the PCB congener composition can be tracked during dechlorination processes. The new model is assessed against four measurements from several published studies on PCB dechlorination. The simulation errors in all four measurements are calculated between 2.67 and 35.1% under minimum (n = 0) and maximum (n = 34) numbers of co-eluters, respectively. The dechlorination fluxes for para-dechlorination pathways dominate PCB dechlorination in all measurements. Furthermore, the model also considers multiple-step dechlorination pathways containing intermediate PCB congeners absent in both the reactants and the products. The present study indicates that redox potential might be an appropriate indicator for predicting PCB dechlorination pathways and fluxes even without prior knowledge of the functional dechlorinating bacteria.

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.

Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction.

Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) heterostructures to unravel the rich landscape of possibilities for enhancing the hydrogen evolution reaction (HER) activity. The stability of the heterostructures and the intercalation within the substrates are verified through adhesion and binding energies, showcasing the significant impact of chalcogenide selection on the interaction properties. Based on hydrogen adsorption Gibbs free energy (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as a robust tool in predicting HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby paving the way for accelerated catalyst screening. The Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and intercalation of TM atoms at the N site (IN). Overall, our integrated approach utilizing DFT and ML effectively identifies hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity in heterostructures intercalated with Sc and Ti, underscoring their potential for advancing catalytic performance.

Novel Insights from Clinical Practice: Xia-Gibbs Syndrome with Pes Cavus, Conjunctival Melanosis, and Eye Asymmetry due to a de novo AHDC1 Gene Variant - A Case Report and a Brief Review of the Literature.

Introduction: Xia-Gibbs syndrome (OMIM 615829) is a rare developmental disorder, caused by heterozygous de novo variants in the AHDC1 gene. Hallmark features include global developmental delay, facial dysmorphisms, and behavioral problems. To date, more than 250 individuals have been diagnosed worldwide. Case Report: We report a 13-year-old female who, in association with typical features of Xia-Gibbs syndrome, presented with macrocrania, pes cavus, and conjunctival melanosis. Whole-exome sequencing identified a de novo frameshift variant, which had not been reported in the literature before. Conclusion: We summarized the main clinical and phenotypic features of patients described in the literature, and in addition, we discuss another feature found in our patient and observed in other cases described, eye asymmetry, which has never been highlighted, and suggest that it could be part of the typical clinical presentation of this condition.

Fluorescence-Based Protein Stability Monitoring-A Review.

Proteins are large biomolecules with a specific structure that is composed of one or more long amino acid chains. Correct protein structures are directly linked to their correct function, and many environmental factors can have either positive or negative effects on this structure. Thus, there is a clear need for methods enabling the study of proteins, their correct folding, and components affecting protein stability. There is a significant number of label-free methods to study protein stability. In this review, we provide a general overview of these methods, but the main focus is on fluorescence-based low-instrument and -expertise-demand techniques. Different aspects related to thermal shift assays (TSAs), also called differential scanning fluorimetry (DSF) or ThermoFluor, are introduced and compared to isothermal chemical denaturation (ICD). Finally, we discuss the challenges and comparative aspects related to these methods, as well as future opportunities and assay development directions.

Entropy of the Canonical Occupancy (Macro) State in the Quantum Measurement Theory.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is obtained through two approaches: one involves tracing out the environment from a bosonic eigenstate of the combined environment and system of interest (the empirical approach), while the other involves tracing out the environment from the mixed state of the combined environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common "infomechanical" framework.

The Thermomajorization Polytope and Its Degeneracies.

Drawing inspiration from transportation theory, in this work, we introduce the notions of "well-structured" and "stable" Gibbs states and we investigate their implications for quantum thermodynamics and its resource theory approach via thermal operations. It is found that, in the quasi-classical realm, global cyclic state transfers are impossible if and only if the Gibbs state is stable. Moreover, using a geometric approach by studying the so-called thermomajorization polytope, we prove that any subspace in equilibrium can be brought out of equilibrium via thermal operations. Interestingly, the case of some subsystem being in equilibrium can be witnessed via the degenerate extreme points of the thermomajorization polytope, assuming that the Gibbs state of the system is well structured. These physical considerations are complemented by simple new constructions for the polytope's extreme points, as well as for an important class of extremal Gibbs-stochastic matrices.

Solvation Thermodynamics and Its Applications.

In this article, we start by describing a few "definitions" of the solvation processes, which were used in the literature until about 1980. Then, we choose one of these definitions and show that it has a simple molecular interpretation. This fact led to a new definition of the solvation process and the corresponding thermodynamic quantities. The new measure of the solvation Gibbs energy has a simple interpretation. In addition, the thermodynamic quantities associated with the new solvation process have several other advantages over the older measures. These will be discussed briefly in the third section. In the fourth section, we discuss a few applications of the new solvation process.

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI.

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) can be used as a non-invasive method for the assessment of myocardial perfusion. The acquired images can be utilised to analyse the spatial extent and severity of myocardial ischaemia (regions with impaired microvascular blood flow). In the present paper, we propose a novel generalisable spatio-temporal hierarchical Bayesian model (GST-HBM) to automate the detection of ischaemic lesions and improve the in silico prediction accuracy by systematically integrating spatio-temporal context information. We present a computational inference procedure with an adequate trade-off between accuracy and computational efficiency, whereby model parameters are sampled from the posterior distribution with Gibbs sampling, while lower-level hyperparameters are selected using model selection strategies based on the Watanabe Akaike information criterion (WAIC). We have assessed our method on both synthetic (in silico) data with known gold-standard and 12 sets of clinical first-pass myocardial perfusion DCE-MRI datasets. We have also carried out a comparative performance evaluation with four established alternative methods: Gaussian mixture model (GMM), opening and closing operations based on Gaussian mixture model (GMMC&Omax), Markov random field constrained Gaussian mixture model (GMM-MRF) and model-based hierarchical Bayesian model (M-HBM). Our results show that the proposed GST-HBM method achieves much higher in silico prediction accuracy than the established alternative methods. Furthermore, this method appears to provide a more robust delineation of ischaemic lesions in datasets affected by spatially variant noise.