Simulation Study Research Articles

In silico Designing of a Multi-epitope-based Subunit Vaccine against SARS-CoV-2 (Delta Variant) by Exploiting Its Structural Proteins: A Reverse Vaccinomics and Immunoinformatics Approach

Background: The continuously emerging novel strains of SARS-CoV-2 remain a menace to the global population. The vicious delta variant (originated in India) is considered one of the most infectious/contagious variants of SARS-CoV-2. The transmission frequency of this variant is 225% higher than other variants, extending its prevalence and causing a massive surge in the COVID-19 pandemic. It is also the most ravenous variant among others. Objective: Though the delta variant has already disappeared, it could re-emerge/come out at any time with a more powerful strike than earlier. Therefore, to tackle such ferocity, this research is undertaken with a next-generation vaccine development strategy to design a multi-epitope-based subunit vaccine against the delta variant of SARS-CoV-2, which might boost the body's immunity. Materials and Methods: In the present investigation, reverse vaccinomics and immunoinformatics approaches were adopted to create an immune-stimulating prospective vaccine candidate having B cell, helper T cell (Th)/helper T lymphocyte (HTL), cytotoxic T cell (Tc)/cytotoxic T lymphocyte (CTL), and interferon-gamma (IFN-γ) inducing epitopes by exploiting the SARS-CoV-2 (delta variant) (GenBank: MZ724536.1) structural proteins: envelope glycoprotein (E), nucleocapsid phosphoprotein (N), surface glycoprotein (S), and membrane glycoprotein (M). The established vaccine construct was then completed by combining antigenic epitopes with adjuvants and linkers. Subsequently, the 3D model of the suggested vaccine was created and docked with an immune receptor (Toll-Like Receptor-4). A molecular dynamics (MD) simulation study was performed to confirm the binding stability between the vaccine conjugate and TLR4. Later, an immune simulation study was carried out to predict the in silico immune response of the vaccine candidate. To effectively express the developed vaccine in a bacterial system (E. coli), in silico codon optimization and cloning were done in an expression vector to manufacture it on a large scale. Results: According to the computational analysis, the vaccine candidate was found to be highly antigenic while maintaining favorable properties for the human body. Molecular docking and dynamics simulation study between the suggested vaccine construct and TLR4 immune receptor depicted it as extremely efficient and stable, ensuring a proper immunological response within the host cell. Eventually, an in silico immune simulation study of the vaccine candidate demonstrated a robust immune response to vaccine administration. Conclusion: We have hypothesized that the constructed vaccine model is benign, stable, and immunogenic, making it a promising/potent candidate for immune system stimulation against SARSCOV- 2 (DV). Hereof, wet lab-based investigations are needed to justify the competence of the novel vaccine candidate towards the delta variant along with other variants of SARS-CoV-2.

Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors

We develop Bayesian model selection via fractional Bayes factors to simultaneously assess spatial dependence and select regressors in Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) spatial random effects. Selection of covariates and spatial model structure is difficult, as spatial confounding creates a tension between fixed and spatial random effects. Researchers have commonly performed selection separately for fixed and random effects in spatial hierarchical models. Simultaneous selection methods relieve the researcher from arbitrarily fixing one of these types of effects while selecting the other. Notably, Bayesian approaches to simultaneously select covariates and spatial model structure are limited. Our use of fractional Bayes factors allows for selection of fixed effects and spatial model structure under automatic reference priors for model parameters, which obviates the need to specify hyperparameters for priors. We also show the equivalence between two ICAR specifications and derive the minimal training size for the fractional Bayes factor applied to the ICAR model under the reference prior. We perform a simulation study to assess the performance of our approach and we compare results to the Deviance Information Criterion and Widely Applicable Information Criterion. We demonstrate that our fractional Bayes factor approach assigns low posterior model probability to spatial models when data is truly independent and reliably selects the correct covariate structure with highest probability within the model space. Finally, we demonstrate our Bayesian model selection approach with applications to county-level median household income in the contiguous United States and residential crime rates in the neighborhoods of Columbus, Ohio.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

An Insight into the Repurposing of Phytoconstituents obtained from Delhi's Aravalli Biodiversity Park as Antifungal Agents.

The global prevalence of fungal infections is alarming in both the pre- and post- COVID period. Due to a limited number of antifungal drugs, there are hurdles in treatment strategies for fungal infections due to toxic potential, drug interactions, and the development of fungal resistance. All the antifungal targets (existing and newer) and pipeline molecules showing promise against these targets are reviewed. The objective was to predict or repurpose phyto-based antifungal compounds based on a dual target inhibition approach (Sterol-14-α- demethylase and HSP-90) using a case study. In pursuit of repurposing the phytochemicals as antifungal agents, a team of researchers visited Aravalli Biodiversity Park (ABP), Delhi, India, to collect information on available medicinal plants. From 45 plants, a total of 1149 ligands were collected, and virtual screening was performed using Schrodinger Suite 2016 software to get 83 hits against both the target proteins: Sterol-14-α-demethylase and HSP-90. After analysis of docking results, ligands were selected based on their interaction against both the target proteins and comparison with respective standard ligands (fluconazole and ganetespib). We have selected Isocarthamidin, Quercetin and Boeravinone B based on their docking score and binding interaction against the HSP-90 (Docking Score -9.65, -9.22 and -9.21, respectively) and 14-α-demethylase (Docking Score -9.19, -10.76 and -9.74 respectively). The docking protocol was validated and MM/GBSA studies depicted better stability of selected three ligands (Isocarthamidin, Quercetin, Boeravinone B) complex as compared to standard complex. Further, MD simulation studies were performed using the Desmond (67) software package version 2018-4. All the findings are presented as a case study for the prediction of dual targets for the repurposing of certain phytochemicals as antifungal agents.

What measures are needed to achieve a tobacco endgame target? A Singapore-based simulation study

BackgroundAn increasing number of countries are pursuing a tobacco ‘endgame’. We sought to determine the combination of measures it would take to achieve a tobacco endgame in the city-state of...

Extending the actor-partner interdependence model to accommodate multivariate dyadic data using latent variables.

This study extends the traditional Actor-Partner Interdependence model (APIM; Kenny, 1996) to incorporate dyadic data with multiple indicators reflecting latent constructs. Although the APIM has been widely used to model interdependence in dyads, the method and its applications have largely been limited to single sets of manifest variables. This article presents three extensions of the APIM that can be applied to multivariate dyadic data; a manifest APIM linking multiple indicators as manifest variables, a composite-score APIM relating univariate sums of multiple variables, and a latent APIM connecting underlying constructs of multiple indicators. The properties of the three methods in analyzing data with various dyadic patterns are investigated through a simulation study. It is found that the latent APIM adequately estimates dyadic relationships and holds reasonable power when measurement reliability is not too low, whereas the manifest APIM yields poor power and high type I error rates in general. The composite-score APIM, even though it is found to be a better alternative to the manifest APIM, fails to correctly reflect latent dyadic interdependence, raising inferential concerns. We illustrate the APIM extensions for multivariate dyadic data analysis by an example study on relationship commitment and happiness among married couples in Wisconsin. In cases where the measures are reliable reflections of psychological constructs, we suggest using the latent APIM for examining research hypotheses that discuss implications beyond observed variables. We conclude with stressing the importance of carefully examining measurement models when designing and conducting dyadic data analyses. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator.

Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE allele, a commonly associated variant of Alzheimer's. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.

In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly used penalties such as the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP), the classical Newton-Raphson algorithm cannot be used. In this article, we propose a cubic Hermite interpolation penalty (CHIP) with a smoothing thresholding operator. Theoretically, we establish the nonasymptotic estimation error bounds for the global minimizer of the CHIP penalized high-dimensional linear regression. Moreover, we show that the estimated support coincides with the target support with a high probability. We derive the Karush-Kuhn-Tucker (KKT) condition for the CHIP penalized estimator and then develop a support detection-based Newton-Raphson (SDNR) algorithm to solve it. Simulation studies demonstrate that the proposed method performs well in a wide range of finite sample situations. We also illustrate the application of our method with a real data example.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

Structure Induced by a Multiple Membership Transformation on the Conditional Autoregressive Model

The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al., 2020; Gramatica et al., 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to researchers. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations.

Bayesian Optimal Two-Sample Tests for High-Dimensional Gaussian Populations

We propose minimax optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including genomics and medical imaging, it is natural to assume that only a few entries of two mean vectors or covariance matrices are different. Many existing tests that rely on aggregating the difference between empirical means or covariance matrices are not optimal or yield low power under such setups. Motivated by this, we develop Bayesian two-sample tests employing a divide-and-conquer idea, which is powerful especially when the differences between two populations are rare but large. The proposed two-sample tests manifest closed forms of Bayes factors and allow scalable computations even in high-dimensions. We prove that the proposed tests are consistent under relatively mild conditions compared to existing tests in the literature. Furthermore, the testable regions from the proposed tests turn out to be minimax optimal in terms of rates. Simulation studies show clear advantages of the proposed tests over other state-of-the-art methods in various scenarios. Our tests are also applied to the analysis of the gene expression data of two cancer data sets.

Consistent and Scalable Bayesian Joint Variable and Graph Selection for Disease Diagnosis Leveraging Functional Brain Network

We consider the joint inference of regression coefficients and the inverse covariance matrix for covariates in high-dimensional probit regression, where the predictors are both relevant to the binary response and functionally related to one another. A hierarchical model with spike and slab priors over regression coefficients and the elements in the inverse covariance matrix is employed to simultaneously perform variable and graph selection. We establish joint selection consistency for both the variable and the underlying graph when the dimension of predictors is allowed to grow much larger than the sample size, which is the first theoretical result in the Bayesian literature. A scalable Gibbs sampler is derived that performs better in high-dimensional simulation studies compared with other state-of-art methods. We illustrate the practical impact and utilities of the proposed method via a functional MRI dataset, where both the regions of interest with altered functional activities and the underlying functional brain network are inferred and integrated together for stratifying disease risk.

Experimental and Simulation Study on Fast Cooling Method of Pulsed Magnetic Coil Based on Heat Pipe

Experimental and Simulation Study on Fast Cooling Method of Pulsed Magnetic Coil Based on Heat Pipe

Distributed Estimator-Based Event-Triggered Neuro-Adaptive Control for Leader-Follower Consensus of Strict-Feedback Nonlinear Multiagent Systems.

This article investigates the leader-follower consensus problem for strict-feedback nonlinear multiagent systems under a dual-terminal event-triggered mechanism. Compared with the existing event-triggered recursive consensus control design, the primary contribution of this article is the development of a distributed estimator-based event-triggered neuro-adaptive consensus control methodology. In particular, by introducing a dynamic event-triggered communication mechanism without continuous monitoring neighbors' information, a novel distributed event-triggered estimator in chain form is constructed to provide the leader's information to the followers. Subsequently, the distributed estimator is utilized to consensus control via backstepping design. To further decrease information transmission, a neuro-adaptive control and an event-triggered mechanism setting on the control channel are codesigned via the function approximate approach. A theoretical analysis shows that all the closed-loop signals are bounded under the developed control methodology, and the estimation of the tracking error asymptotically converges to zero, i.e., the leader-follower consensus is guaranteed. Finally, simulation studies and comparisons are conducted to verify the effectiveness of the proposed control method.

Structure-based Virtual Screening and Molecular Dynamic Simulation Approach for the Identification of Terpenoids as Potential DPP-4 Inhibitors.

Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery. In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus. Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions. According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies. Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.

Adaptive Decentralized Control for Constrained Strong Interconnected Nonlinear Systems and Its Application to Inverted Pendulum.

This work is dedicated to adaptive decentralized tracking control for a class of strong interconnected nonlinear systems with asymmetric constraints. Currently, there are few related studies on unknown strongly interconnected nonlinear systems with asymmetric time-varying constraints. To deal with the assumptions of the interconnection terms in the design process, which include upper functions and structural restrictions, the properties of Gaussian function in radial basis function (RBF) neural networks are applied to overcome this difficulty. By constructing the nonlinear state-dependent function (NSDF) and using a new coordinate transformation, the conservative step that the original state constraint converts into a new boundary of the tracking error is removed. Meanwhile, the virtual controller's feasibility condition is removed. It is proven that all the signals are bounded, especially the original tracking error and the new tracking error, which are both bounded. In the end, simulation studies are carried out to verify the effectiveness and benefits of the proposed control scheme.

Asking for less (but receiving more): Women avoid impasses and outperform men when negotiators have weak alternatives.

Both research and conventional wisdom suggest that, due to their relational orientation, women are less likely than men to engage in agentic and assertive behaviors, leading them to underperform in zero-sum, distributive negotiations where one party's gain is equivalent to the other party's loss. However, past research tends to neglect the costs of reaching impasse by excluding impasses from measures of negotiation performance. Departing from this convention, we incorporate the economic costs of impasses into measures of negotiation performance to provide a more holistic examination of negotiation outcomes. In so doing, we reveal a reversal of the oft-cited male performance advantage when obtaining an impasse is especially economically costly (as is the case when negotiators have weak negotiation alternatives). Specifically, we predicted that female negotiators would make less assertive first offers than men due to their more relational orientation and that these gender differences in offer assertiveness should result in women avoiding impasse more often than men. Since avoiding impasses should improve negotiation performance when negotiators are able to obtain a deal that is more valuable than their negotiation alternative, women's tendency to avoid impasses should improve their performance when negotiators have weak (vs. strong) alternatives. These predictions were supported in eight studies (three preregistered) across various negotiation contexts, comprising data from the television show Shark Tank (Study 1), four incentive-compatible negotiation simulations (Studies 2 and 3, Supplemental Studies), and a multistudy causal experimental chain (Supplemental Studies 4a-c). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Simulation Study of Clinical PET Scanners With Different Geometries, Including TOF and DOI Capabilities

Simulation Study of Clinical PET Scanners With Different Geometries, Including TOF and DOI Capabilities