Heterogeneous Parameters Research Articles

The Hopf whole-brain model and its linear approximation

Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation—that holds in the case of heterogeneous parameters and time-delays—allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.

Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems

We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogeneous parameter fields. These fields are approximated using low-dimensional conditional Karhunen-Loève expansions (CKLEs), which are constructed using Gaussian process regression (GPR) models of these fields trained on the parameters' measurements. We then assimilate measurements of the state of the system and compute the maximum a posteriori (MAP) estimate of the CKLE coefficients by solving a nonlinear least-squares problem. When solving this optimization problem, we efficiently compute the Jacobian of the vector objective by exploiting the sparsity structure of the linear system of equations associated with the forward solution of the physics problem.The CKLEMAP method provides better scalability compared to the standard MAP method. In the MAP method, the number of unknowns to be estimated is equal to the number of elements in the numerical forward model. On the other hand, in CKLEMAP, the number of unknowns (CKLE coefficients) is controlled by the smoothness of the parameter field and the number of measurements, and is generally much smaller than the number of discretization nodes, which leads to a significant reduction of computational cost with respect to the standard MAP method. To show this advantage in scalability, we apply CKLEMAP to estimate the transmissivity field in a two-dimensional steady-state subsurface flow model of the Hanford Site by assimilating synthetic measurements of transmissivity and hydraulic head. We find that the execution time of CKLEMAP scales nearly linearly as N1.33, where N is the number of discretization nodes, while the execution time of standard MAP scales as N2.91. The CKLEMAP method improved execution time without sacrificing accuracy when compared to the standard MAP method.

Potentially Harmful Excipients: State of the Art for Oral Liquid Forms Used in Neonatology and Pediatrics Units.

The pediatric population exhibits an important age-dependent heterogeneity in pharmacokinetics and pharmacodynamics parameters, resulting in differences in drug efficacy and toxicity compared to the adult population, particularly for neonates. Toxicity and efficacy divergences have been studied for active molecules, but the impact on the pharmacological parameters of excipients remains less well known. To fill this lack of knowledge, several initiatives have been started to gather information on the specific toxicity of excipients, such as the KIDS list or the STEP database. In order to contribute to this much-needed action, in this work, a compilation of the 219 formulations of oral liquid forms prescribed in pediatrics and neonatology units was established based on the summary of product characteristics. Then, for excipients found in more than 10% of the analyzed formulations, a review of their toxicity data was carried out using the STEP database. Finally, for a selection of 10 frequently used liquid forms, the amounts of excipients administered daily were calculated based on the recommended posology in the Summary of Product Characteristics (SPC) and compared with the recommended daily limits proposed by the European Medicine Agency. Pediatrics-adapted formulations are still rare, and it is not always possible to find safe alternatives to drugs containing excipients of interest.

Toolkits for detailed and high-throughput interrogation of synapses in C. elegans.

Visualizing synaptic connectivity has traditionally relied on time-consuming electron microscopy-based imaging approaches. To scale the analysis of synaptic connectivity, fluorescent protein-based techniques have been established, ranging from the labeling of specific pre- or post-synaptic components of chemical or electrical synapses to transsynaptic proximity labeling technology such as GRASP and iBLINC. In this paper, we describe WormPsyQi, a generalizable image analysis pipeline that automatically quantifies synaptically localized fluorescent signals in a high-throughput and robust manner, with reduced human bias. We also present a resource of 30 transgenic strains that label chemical or electrical synapses throughout the nervous system of the nematode Caenorhabditis elegans, using CLA-1, RAB-3, GRASP (chemical synapses), or innexin (electrical synapse) reporters. We show that WormPsyQi captures synaptic structures in spite of substantial heterogeneity in neurite morphology, fluorescence signal, and imaging parameters. We use these toolkits to quantify multiple obvious and subtle features of synapses - such as number, size, intensity, and spatial distribution of synapses - in datasets spanning various regions of the nervous system, developmental stages, and sexes. Although the pipeline is described in the context of synapses, it may be utilized for other 'punctate' signals, such as fluorescently tagged neurotransmitter receptors and cell adhesion molecules, as well as proteins in other subcellular contexts. By overcoming constraints on time, sample size, cell morphology, and phenotypic space, this work represents a powerful resource for further analysis of synapse biology in C. elegans.

Toolkits for detailed and high-throughput interrogation of synapses in C. elegans

Visualizing synaptic connectivity has traditionally relied on time-consuming electron microscopy-based imaging approaches. To scale the analysis of synaptic connectivity, fluorescent protein-based techniques have been established, ranging from the labeling of specific pre- or post-synaptic components of chemical or electrical synapses to transsynaptic proximity labeling technology such as GRASP and iBLINC. In this paper, we describe WormPsyQi, a generalizable image analysis pipeline that automatically quantifies synaptically localized fluorescent signals in a high-throughput and robust manner, with reduced human bias. We also present a resource of 30 transgenic strains that label chemical or electrical synapses throughout the nervous system of the nematode Caenorhabditis elegans, using CLA-1, RAB-3, GRASP (chemical synapses), or innexin (electrical synapse) reporters. We show that WormPsyQi captures synaptic structures in spite of substantial heterogeneity in neurite morphology, fluorescence signal, and imaging parameters. We use these toolkits to quantify multiple obvious and subtle features of synapses – such as number, size, intensity, and spatial distribution of synapses – in datasets spanning various regions of the nervous system, developmental stages, and sexes. Although the pipeline is described in the context of synapses, it may be utilized for other ‘punctate’ signals, such as fluorescently tagged neurotransmitter receptors and cell adhesion molecules, as well as proteins in other subcellular contexts. By overcoming constraints on time, sample size, cell morphology, and phenotypic space, this work represents a powerful resource for further analysis of synapse biology in C. elegans.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. Retrospective. 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. 3 TECHNICAL EFFICACY: Stage 2.

A comprehensive study of how urban morphological parameters impact the solar potential, energy consumption and daylight autonomy in canyons and buildings

Over the past two decades, global greenhouse gas emissions have surged, leading to rising sea levels and climate change. Solar energy has emerged as an excellent solution to this problem. This study aims to assess daylight availability and energy consumption in urban canyons and blocks in a semi-arid climate while exploring the correlation between the heterogeneity of urban morphological parameters and key research variables. The study employed field measurements and simulations using Ladybug Tools and UMI. Data were collected from urban canyons and blocks in three areas, amounting to 1,366,982 m2. The findings indicate that areas with abundant greenery and H/W ratios exceeding 1.8 experience the most significant shading effects in urban canyons. Selecting suitable trees and placing them in strategic locations can enhance energy efficiency. Furthermore, broad urban canyons (H/W < 0.5) receive four times more solar radiation than narrow urban canyons (H/W > 1.8) with high tree density, peaking at 240 kWh/m2 during summer. In urban blocks, rooftops have the highest solar potential, with imbalances between building heights and interbuilding shading as factors that reduce solar capabilities. Tree coverage significantly affects the solar potential of facades, with some urban blocks receiving less than 5,000 kWh/m2 of solar radiation on the southern facades each month. Finally, this study examined the impact of heterogeneity within urban morphological parameters on the principal research variables. The results indicate that the heterogeneity observed in urban morphological parameters affects daylight availability and energy consumption within urban blocks.

Bayesian meta-analysis of phase 3 results of aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease.

Phase 3 trials using the anti-amyloid antibodies aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease dementia were heterogeneous in respect to statistical significance of effects. However, heterogeneity of results has not yet directly be quantified. We used Bayesian random effects meta-analysis to quantify evidence for or against a treatment effect, and assessed the size of the effect and its heterogeneity. Data were extracted from published studies where available and Web based data reports, assuming a Gaussian data generation process. We found moderate evidence in favor of a treatment effect (Bayes factor = 13.2). The effect was moderate to small with -0.33 (95% credible interval -0.54 to -0.10) points on the Clinical Dementia Rating - Sum of Boxes (CDR-SB) scale. The heterogeneity parameter was low to moderate with 0.21 (0.04 to 0.45) CDR-SB points. Heterogeneity across studies was moderate despite some trials reaching statistical significance, while others did not. This suggests that the negative aducanumab and gantenerumab trials are in full agreement with the expected effect sizes.

Seasonal Fluctuations and Vertical Heterogeneity of Biochemical-Structural Parameters in Wetland Emergent Aquatic Vegetation.

Accurate understanding of vertical patterns of canopy structure characteristics and solar radiation distribution patterns of aquatic vegetation is pivotal in formulating a bidirectional reflection model and comprehending the ecological dynamics of wetlands. Further, physiological and biochemical stratified structural properties of aquatic vegetation in wetlands remain unexplored due to more inherent investigation challenges than terrestrial vegetation. This study evaluated the structural characteristics of vegetation communities and the regulation of direct solar radiation variations within the canopy across seasons of Phragmites australis (P. australis) and Typha orientalis (T. orientalis), 2 typical emergent aquatic vegetations (EAVs), based on radiative transfer theory. Observations revealed that physiological and biochemical metrics varied at different growth stages with canopy height, the stratified leaf area index in the middle being higher than at the top and bottom of the P. australis cluster. Moreover, the vertical profiles of direct solar radiation decrease with depth, showing a bowl-shaped and V-shaped curve in the P. australis and T. orientalis clusters, respectively. Interestingly, the sensitivity of layered solar direct radiation transmittance to canopy structural parameters is obviously higher than that of canopy pigments, suggesting considerable potential for estimating layered structural parameters. The transmittance of direct solar radiation decreases with increasing leaf area index at different heights, and stratified transmittance in the cluster can be accurately described by a negative binomial function with a deviation of less than 2%.

Generalities on a Delayed Spatiotemporal Host–Pathogen Infection Model with Distinct Dispersal Rates

We propose a general model to investigate the effect of the distinct dispersal coefficients infected and susceptible hosts in the pathogen dynamics. The mathematical challenge lies in the fact that the investigated model is partially degenerate and the solution map is not compact. The spatial heterogeneity of the model parameters and the distinct diffusion coefficients induce infection in the low-risk regions. In fact, as infection dispersal increases, the reproduction of the pathogen particles decreases. The dynamics of the investigated model is governed by the value of the basic reproduction number R0. If R0 ≤ 1, then the pathogen particles extinct, and for R0 &gt; 1 the pathogen particles persist, and there is at least one positive steady state. The asymptotic profile of the positive steady state is shown in the case when one or both diffusion coefficients for the host tends to zero or infinity.

Exponential Asynchronous Stabilization for Delayed Semi-Markovian Neural Networks via DAEIC.

The exponential asynchronous stabilization (EAS) issue for a category of neural networks (NNs) with semi-Markov jump (SMJ) parameters and additive time-varying delays (ATDs) is addressed in this article. Here, the SMJ parameters in the controller gain are supposed to be distinct from those in the system structure, which is more consistent with the actual situation. To further relieve the communication load of the network, a new discrete adaptive event-triggered impulsive control (DAEIC) scheme is proposed, where the impulsive moments are the sampling instants satisfying event-triggered constraints, and the triggering threshold can be dynamically adjusted by an adaptive update rule (AUR) related to the current sampling state and the last triggered state. A more flexible looped Lyapunov-Krasovski functional (LLKF) is constructed to commendably capture the available information about impulsive instants, triggering state, sampling interval, ATDs, and heterogeneous SMJ parameters. Combined with the LLKF, DAEIC scheme, and other inequality analysis approaches, some novel results guaranteeing the EAS of the underlying systems are exported. Finally, three explanatory examples are presented to check the validity of our results.

Heterogeneities of grain boundary contact for simulation of laboratory-scale mechanical behavior of granitic rocks

From a practical point of view, grain structure heterogeneities are key parameters that control the rock response and still remains a challenge to incorporate in a quantitative manner. One of the less discussed topics in the context of the grain-based model (GBM) in the particle flow code (PFC) is the contact heterogeneities and the appropriate contact model to mimic the grain boundary behavior. Generally, the smooth joint (SJ) model and linear parallel bond (LPB) model are used to simulate the grain boundary behavior. However, the literature does not document the suitability of different models for specific problems. Another challenge in implementing GBM in PFC is that only a single bonding parameter is used at the grain boundaries. The aim of this study is to investigate the responses of a laboratory-scale specimen with SJ and LPB models, considering grain boundary heterogeneous and homogeneous contact parameters. Uniaxial and biaxial compression tests are performed to calibrate the response of Creighton granite. The stress–strain curves, volumetric dilation, inter-crack (crack in the grain boundary), and intra-crack (crack within the grain) development, and failure patterns associated with different contact models are examined. It was found that both the SJ and LPB models can reproduce the pre-peak behavior observed for a granitic rock type. However, the LPB model is unable to reproduce the post-peak behavior. Due to the large interlocking effect originating from the balls in contact and the ball size in the LPB model, local dilation is induced at the grain boundaries. This overestimates the volumetric dilation and residual shear strength. The LPB model tends to result in discontinuous inter-cracks and stress localization in the rock specimen, resulting in fine fragments at the rock surface during failure.

A novel multivariate copula of Raftery type with multiple dependence parameters and its neutrosophic application in finance

This paper introduces an innovative multivariate exponential distribution, specifically of Raftery type, characterized by heterogeneous dependence parameters. Various properties of this distribution family are thoroughly investigated, with particular emphasis placed on the copula derived from this model. Notably, this copula is non-exchangeable and demonstrates multiple dependence parameters. Different properties associated with this novel copula, including the examination of estimation parameters, have been thoroughly investigated. The efficacy of the proposed copula is demonstrated through its successful application in modeling a real neutrosophic dataset associated with the New York and American Stock Exchanges.

Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes.

High-density multielectrode catheters are becoming increasingly popular in cardiac electrophysiology for advanced characterisation of the cardiac tissue, due to their potential to identify impaired sites. These are often characterised by abnormal electrical conduction, which may cause locally disorganised propagation wavefronts. To quantify it, a novel heterogeneity parameter based on vector field analysis is proposed, utilising finite differences to measure direction changes between adjacent cliques. The proposed Vector Field Heterogeneity metric has been evaluated on a set of simulations with controlled levels of organisation in vector maps, and a variety of grid sizes. Furthermore, it has been tested on animal experimental models of isolated Langendorff-perfused rabbit hearts. The proposed parameter exhibited superior capturing ability of heterogeneous propagation wavefronts compared to the classical Spatial Inhomogeneity Index, and simulations proved that the metric effectively captures gradual increments in disorganisation in propagation patterns. Notably, it yielded robust and consistent outcomes for [Formula: see text] grid sizes, underscoring its suitability for the latest generation of orientation-independent cardiac catheters.

Admittance-Based Aggregated Modeling of ConverterInterfaced Resources with Heterogeneous Parameters for Transient Analysis of Multi-Converter Systems

Admittance-Based Aggregated Modeling of ConverterInterfaced Resources with Heterogeneous Parameters for Transient Analysis of Multi-Converter Systems

Equalized Aggregation for Heterogeneous Federated Mobile Edge Learning

Federated Learning (FL) is widely used in mobile edge applications. However, the heterogeneity issues of mobile edge devices pose significant challenges to the generalization of the global model in FL. In this paper, we propose LegoFL to simultaneously solve multiple heterogeneity issues in response to mobile edge computing characteristics. LegoFL identifies two types of heterogeneous behaviors in FL, namely heterogeneous parameter training and communication behaviors, to address multiple heterogeneity issues. These two types of heterogeneous behaviors result in feature and feature representation range mismatches between local communication parameters. To reduce these mismatches and improve the generalization of the global model, LegoFL dynamically distinguishes the parameter feature representation of different nodes using the global model's common feature as guidance. Then, under the connection states and system communication constraints, LegoFL dynamically selects contribution parameters on each device that can guarantee the generalization and performance of the global model for communication. Finally, to avoid the overfitting problem of the global model, heterogeneous local models are aggregated at the central server with matched feature representations. Extensive experiments on various datasets show that LegoFL achieves competitive performance. The accuracy and communication efficiency are improved by up to 12.86 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 4.09× compared to state-of-the-art approaches.

Pattern recognition of microcirculation with super-resolution ultrasound imaging provides markers for early tumor response to anti-angiogenic therapy.

Rationale: Cancer treatment outcome is traditionally evaluated by tumor volume change in clinics, while tumor microvascular heterogeneity reflecting tumor response has not been fully explored due to technical limitations. Methods: We introduce a new paradigm in super-resolution ultrasound imaging, termed pattern recognition of microcirculation (PARM), which identifies both hemodynamic and morphological patterns of tumor microcirculation hidden in spatio-temporal space trajectories of microbubbles. Results: PARM demonstrates the ability to distinguish different local blood flow velocities separated by a distance of 24 μm. Compared with traditional vascular parameters, PARM-derived heterogeneity parameters prove to be more sensitive to microvascular changes following anti-angiogenic therapy. Particularly, PARM-identified "sentinel" microvasculature, exhibiting evident structural changes as early as 24 hours after treatment initiation, correlates significantly with subsequent tumor volume changes (|r| > 0.9, P < 0.05). This provides prognostic insight into tumor response much earlier than clinical criteria. Conclusions: The ability of PARM to noninvasively quantify tumor vascular heterogeneity at the microvascular level may shed new light on early-stage assessment of cancer therapy.

VARIATIONS IN MAGNETIC PROPERTIES PROPAGATION IN STEEL SHELL STRUCTURE CAUSED BY WELDING (PART 2)

The first stage of the research involves analyzing primary and secondary acoustic wave propagation behavior. The results of the first stage suggest that the properties of shell structures used at every stage of the experiment are distributed unevenly along the generator lines and across the width of the test pipes. In order to conduct a comparative study of properties, the authors of the research introduce the heterogeneity parameter. During the experiment, the parameter is changing locally within two orders of magnitude, even in case of test pipes in their initial state. Equal sensitivity to heterogeneities across steel products is found in its magnetic properties. As numerous studies prove, analyzing variations in magnetic properties makes it possible to detect flaws and areas of their concentration. This method can also be used to estimate the remaining service life. This is a crucial aspect to consider when assessing properties and using the findings to develop digital twins of shell structures. The authors focus on identifying acoustic and magnetic signals coming from flawed shell structures and weld joint areas in particular. For this purpose, magnetic memory method and X-ray analysis are used. It is found that both normal and tangential magnetic field components are sensitive to structural heterogeneities across a steel product. The combination of magnetic memory method and X-ray analysis make it possible to determine how typical weld joint flaws occurring during welding affect the magnetic properties. Sufficient measurements are required for statistical analysis of the findings. It is also possible to introduce further criteria in order to import metal properties correctly into the modelling program using numerical methods.

Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.