Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Revisiting the evolution of bow-tie architecture in signaling networks.

Bow-tie architecture is a layered network structure that has a narrow middle layer with multiple inputs and outputs. Such structures are widely seen in the molecular networks in cells, suggesting that a universal evolutionary mechanism underlies the emergence of bow-tie architecture. The previous theoretical studies have implemented evolutionary simulations of the feedforward network to satisfy a given input-output goal and proposed that the bow-tie architecture emerges when the ideal input-output relation is given as a rank-deficient matrix with mutations in network link intensities in a multiplicative manner. Here, we report that the bow-tie network inevitably appears when the link intensities representing molecular interactions are small at the initial condition of the evolutionary simulation, regardless of the rank of the goal matrix. Our dynamical system analysis clarifies the mechanisms underlying the emergence of the bow-tie structure. Further, we demonstrate that the increase in the input-output matrix reduces the width of the middle layer, resulting in the emergence of bow-tie architecture, even when evolution starts from large link intensities. Our data suggest that bow-tie architecture emerges as a side effect of evolution rather than as a result of evolutionary adaptation.

Coordination, cooperation, competition, crowding and congestion of molecular motors: Theoretical models and computer simulations.

Cytoskeletal motor proteins are biological nanomachines that convert chemical energy into mechanical work to carry out various functions such as cell division, cell motility, cargo transport, muscle contraction, beating of cilia and flagella, and ciliogenesis. Most of these processes are driven by the collective operation of several motors in the crowded viscous intracellular environment. Imaging and manipulation of the motors with powerful experimental probes have been complemented by mathematical analysis and computer simulations of the corresponding theoretical models. In this article, we illustrate some of the key theoretical approaches used to understand how coordination, cooperation and competition of multiple motors in the crowded intra-cellular environment drive the processes that are essential for biological function of a cell. In spite of the focus on theory, experimentalists will also find this article as an useful summary of the progress made so far in understanding multiple motor systems.

Sample Size Determination and Study Design Impact on Dose-Scale Pharmacodynamic Bioequivalence: a Case Study Using Orlistat.

Dose-scale pharmacodynamic bioequivalence is recommended for evaluating the consistency of generic and innovator formulations of certain locally acting drugs, such as orlistat. This study aimed to investigate the standard methodology for sample size determination and the impact of study design on dose-scale pharmacodynamic bioequivalence using orlistat as the model drug. A population pharmacodynamic model of orlistat was developed using NONMEM 7.5.1 and utilized for subsequent simulations. Three different study designs were evaluated across various predefined relative bioavailability ratios of test/reference (T/R) formulations. These designs included Study Design 1 (2×1 crossover with T1 60 mg, R1 60 mg, and R2 120 mg), Study Design 2 (2×1 crossover with T2 120 mg, R1 60 mg, and R2 120 mg), and Study Design 3 (2×2 crossover with T1 60 mg, T2 120 mg, R1 60 mg, and R2 120 mg). Sample sizes were determined using a stochastic simulation and estimation approach. Under the same T/R ratio and power, Study Design 3 required the minimum sample size for bioequivalence, followed by Study Design 1, while Study Design 2 performed the worst. For Study Designs 1 and 3, a larger sample size was needed on the T/R ratio < 1.0 side for the same power compared to that on the T/R ratio > 1.0 side. The opposite asymmetry was observed for Study Design 2. We demonstrated that Study Design 3 is most effective for reducing the sample size for orlistat bioequivalence studies, and the impact of T/R ratio on sample size shows asymmetry.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Optimized design of planar solid oxide fuel cell interconnectors.

Solid oxide fuel cells (SOFCs) are vital for alternative energy, powering motors effi-ciently. They offer fuel versatility and waste heat recovery, making them ideal for various applications. Optimizing interconnector structures is crucial for SOFC advancement. This paper introduces a novel 2D simulation model for interconnector SOFCs, aiming to enhance their performance. We initially construct a single half-cell model for a conventional interconnector SOFC, ensuring model accuracy. Subsequently, we propose an innovative interconnector SOFC model, which outperforms the conventional counterpart in various aspects.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus.

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

Antiviral activity of luteolin against porcine epidemic diarrhea virus in silico and in vitro.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV) mainly causes acute and severe porcine epidemic diarrhea (PED), and is highly fatal in neonatal piglets. No reliable therapeutics against the infection exist, which poses a major global health issue for piglets. Luteolin is a flavonoid with anti-viral activity toward several viruses. RESULTS: We evaluated anti-viral effects of luteolin in PEDV-infected Vero and IPEC-J2 cells, and identified IC50 values of 23.87 µM and 68.5 µM, respectively. And found PEDV internalization, replication and release were significantly reduced upon luteolin treatment. As luteolin could bind to human ACE2 and SARS-CoV-2 main protease (Mpro) to contribute viral entry, we first identified that luteolin shares the same core binding site on pACE2 with PEDV-S by molecular docking and exhibited positive pACE2 binding with an affinity constant of 71.6 µM at dose-dependent increases by surface plasmon resonance (SPR) assay. However, pACE2 was incapable of binding to PEDV-S1. Therefore, luteolin inhibited PEDV internalization independent of PEDV-S binding to pACE2. Moreover, luteolin was firmly embedded in the groove of active pocket of Mpro in a three-dimensional docking model, and fluorescence resonance energy transfer (FRET) assays confirmed that luteolin inhibited PEDV Mpro activity. In addition, we also observed PEDV-induced pro-inflammatory cytokine inhibition and Nrf2-induced HO-1 expression. Finally, a drug resistant mutant was isolated after 10 cell culture passages concomitant with increasing luteolin concentrations, with reduced PEDV susceptibility to luteolin identified at passage 10. CONCLUSIONS: Our results push forward that anti-PEDV mechanisms and resistant-PEDV properties for luteolin, which may be used to combat PED.

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

In silico analysis of the impact of toxic metals on COVID-19 complications: molecular insights.

COVID-19 can cause a range of complications, including cardiovascular, renal, and/or respiratory insufficiencies, yet little is known of its potential effects in persons exposed to toxic metals. The aim of this study was to answer this question with in silico toxicogenomic methods that can provide molecular insights into COVID-19 complications owed to exposure to arsenic, cadmium, lead, mercury, nickel, and chromium. For this purpose we relied on the Comparative Toxicogenomic Database (CTD), GeneMANIA, and ToppGene Suite portal and identified a set of five common genes (IL1B, CXCL8, IL6, IL10, TNF) for the six metals and COVID-19, all of which code for pro-inflammatory and anti-inflammatory cytokines. The list was expanded with additional 20 related genes. Physical interactions are the most common between the genes affected by the six metals (77.64 %), while the dominant interaction between the genes affected by each metal separately is co-expression (As 56.35 %, Cd 64.07 %, Pb 71.5 %, Hg 81.91 %, Ni 64.28 %, Cr 88.51 %). Biological processes, molecular functions, and pathways in which these 25 genes participate are closely related to cytokines and cytokine storm implicated in the development of COVID-19 complications. In other words, our findings confirm that exposure to toxic metals, alone or in combinations, might escalate COVID-19 severity.

Estimated effects of reductions in processed meat consumption and unprocessed red meat consumption on occurrences of type 2 diabetes, cardiovascular disease, colorectal cancer, and mortality in the USA: a microsimulation study.

BACKGROUND: High consumption of processed meat and unprocessed red meat is associated with increased risk of multiple chronic diseases, although there is substantial uncertainty regarding the relationship for unprocessed red meat. We developed a microsimulation model to estimate how reductions in processed meat and unprocessed red meat consumption could affect rates of type 2 diabetes, cardiovascular disease, colorectal cancer, and mortality in the US adult population. METHODS: We used data from two versions of the US National Health and Nutrition Examination Survey, one conducted during 2015-16 and one conducted during 2017-18, to create a simulated US population. The starting cohort was restricted to respondents aged 18 years or older who were not pregnant and had 2 days of dietary-recall data. First, we used previously developed risk models to estimate the baseline disease risk of an individual. For type 2 diabetes we used a logistic-regression model and for cardiovascular disease and colorectal cancer we used Cox proportional-hazard models. We then multiplied baseline risk by relative risk associated with individual processed meat and unprocessed red meat consumption. Prevented occurrences of type 2 diabetes, cardiovascular disease, colorectal cancer, and mortality were computed by taking the difference between the incidence in the baseline and intervention scenarios. All stages were repeated for ten iterations to correspond to a 10-year time span. Scenarios were reductions of 5%, 10%, 30%, 50%, 75%, and 100% in grams consumed of processed meat, unprocessed red meat, or both. Each scenario was repeated 50 times for uncertainty analysis. FINDINGS: The total number of individual respondents included in the simulated population was 8665, representing 242 021 876 US adults. 4493 (51·9%) of 8665 individuals were female and 4172 (48·1%) were male; mean age was 49·54 years (SD 18·38). At baseline, weighted mean daily consumption of processed meat was 29·1 g, with a 30% reduction being 8·7 g per day, and of unprocessed red meat was 46·7 g, with a 30% reduction being 14·0 g per day. We estimated that a 30% reduction in processed meat intake alone could lead to 352 900 (95% uncertainty interval 345 500-359 900) fewer occurrences of type 2 diabetes, 92 500 (85 600-99 900) fewer occurrences of cardiovascular disease, 53 300 (51 400-55 000) fewer occurrences of colorectal cancer, and 16 700 (15 300-17 700) fewer all-cause deaths during the 10-year period. A 30% reduction in unprocessed red meat intake alone could lead to 732 600 (725 700-740 400) fewer occurrences of type 2 diabetes, 291 500 (283 900-298 800) fewer occurrences of cardiovascular disease, 32 200 (31 500-32 700) fewer occurrences of colorectal cancer, and 46 100 (45 300-47 200) fewer all-cause deaths during the 10-year period. A 30% reduction in both processed meat and unprocessed red meat intake could lead to 1 073 400 (1 060 100-1 084 700) fewer occurrences of type 2 diabetes, 382 400 (372 100-391 000) fewer occurrences of cardiovascular disease, 84 400 (82 100-86 200) fewer occurrences of colorectal cancer, and 62 200 (60 600-64 400) fewer all-cause deaths during the 10-year period. INTERPRETATION: Reductions in processed meat consumption could reduce the burden of some chronic diseases in the USA. However, more research is needed to increase certainty in the estimated effects of reducing unprocessed red meat consumption. FUNDING: The Wellcome Trust.

The effect on the equilibrium sickle cell allele frequency of the probable protection conferred by malaria and sickle cell gene against other infectious diseases.

If a mutated gene with heterozygous advantage against malaria, e.g., hemoglobin S (HbS) gene, is introduced in a small tribe, the gene (allele) frequency (fgene) increases until it reaches a steady state value (feq) where the total mortality from malaria and sickle cell disease is a minimum. This is a classic example of balanced-polymorphism named malaria hypothesis. In a previous in silico study, assuming realistic initial conditions, it has been shown that the feq is around 14%, far less than the fgene observed in certain parts of Africa, 24%. It seems that the malaria hypothesis, per se, could not explain such a high fgene, unless it is assumed that malaria and HbS gene can provide protection against other diseases. Using Monte-Carlo simulation, the current study was conducted to examine the effect on feq of five scenarios was examined. The studied scenarios consisted of different combinations of mortality of other diseases and the possible amounts of protections conferred by malaria and HbS gene against the diseases. Taking into account other diseases causing mortality in the population makes the fgene rate of change steeper over generations. feq is an increasing function of the amount of protection conferred by HbS gene against other diseases. The effect of protection provided by malaria against other diseases on feq, is however, variable-depending on the amount of protection conferred by HbS gene against other diseases, it may increase or decrease feq. If malaria and HbS gene provide protections of 1.5-fold and threefold against other diseases, respectively, the feq is around 24%, the amount reported in certain tribes of Africa. Under certain scenarios, the feq attained is even higher.

It's about time: mitigating cancer-related cognitive impairments through findings from computational models of the Wisconsin Card Sorting Task.

BACKGROUND: Many cancer survivors experience cancer-related cognitive impairment (CRCI), often with significant negative consequences across various life domains. Emerging evidence suggests that allowing additional time to process information before acting may be a useful strategy for those with CRCI to mitigate some of its impacts. The Wisconsin Card Sorting Task (WCST), a measure of general cognition, has shown that for some cancer survivors, longer task completion time facilitates similar task performance outcomes to control populations concerning perseveration errors; a key performance metric of the WCST. However, assessing if this strategy may be useful, as well as determining for whom it may be useful, with regard to strengths and weaknesses among select cognitive domains, is challenging due to factors such as the problem of task impurity. Accordingly, this study provides an initial computational and experimental assessment of whether additional time to process information before acting is a useful strategy for those with CRCI. METHODS: We simulated individual cognitive differences observed in humans by varying contributions of executive functioning components (updating, shifting, inhibition) to yield 48 distinct computational models of the WCST. Our main manipulation was then to provide these models with more or less time (at three levels of 20, 40 and 60 cycles) before models executed an action to sort a given card. We compared the number of perseveration errors on the WCST produced by the computational models. Additionally, we determined models that simulated the performance of cancer survivors on the WCST by comparing the number of perseveration errors produced by the models to human data. RESULTS: Additional processing time resulted in the models producing significantly fewer perseveration errors, supporting our hypothesis. In addition, 8 unique models simulated the performance of cancer survivors on the WCST. Additional time appeared to have a positive influence on performance primarily by mitigating the impacts of severe inhibition impairments. For more severe global executive function impairments, a substantial amount of additional time was required to mitigate the impacts of the impairments. For the most severe impairments, additional time was unable to adequately mitigate the impact on performance. CONCLUSION: Additional processing time may be a useful strategy to rectify perseveration errors among cancer survivors with CRCI. Our findings have implications for the development of practical strategies, such as workload and deadline management in occupational settings, which may mitigate the negative effects of CRCI.

A novel start-loss mutation of the SLC29A3 gene in a consanguineous family with H syndrome: clinical characteristics, in silico analysis and literature review.

BACKGROUND: The SLC29A3 gene, which encodes a nucleoside transporter protein, is primarily located in intracellular membranes. The mutations in this gene can give rise to various clinical manifestations, including H syndrome, dysosteosclerosis, Faisalabad histiocytosis, and pigmented hypertrichosis with insulin-dependent diabetes. The aim of this study is to present two Iranian patients with H syndrome and to describe a novel start-loss mutation in SLC29A3 gene. METHODS: In this study, we employed whole-exome sequencing (WES) as a method to identify genetic variations that contribute to the development of H syndrome in a 16-year-old girl and her 8-year-old brother. These siblings were part of an Iranian family with consanguineous parents. To confirmed the pathogenicity of the identified variant, we utilized in-silico tools and cross-referenced various databases to confirm its novelty. Additionally, we conducted a co-segregation study and verified the presence of the variant in the parents of the affected patients through Sanger sequencing. RESULTS: In our study, we identified a novel start-loss mutation (c.2T > A, p.Met1Lys) in the SLC29A3 gene, which was found in both of two patients. Co-segregation analysis using Sanger sequencing confirmed that this variant was inherited from the parents. To evaluate the potential pathogenicity and novelty of this mutation, we consulted various databases. Additionally, we employed bioinformatics tools to predict the three-dimensional structure of the mutant SLC29A3 protein. These analyses were conducted with the aim of providing valuable insights into the functional implications of the identified mutation on the structure and function of the SLC29A3 protein. CONCLUSION: Our study contributes to the expanding body of evidence supporting the association between mutations in the SLC29A3 gene and H syndrome. The molecular analysis of diseases related to SLC29A3 is crucial in understanding the range of variability and raising awareness of H syndrome, with the ultimate goal of facilitating early diagnosis and appropriate treatment. The discovery of this novel biallelic variant in the probands further underscores the significance of utilizing genetic testing approaches, such as WES, as dependable diagnostic tools for individuals with this particular condition.

Research on control strategy of pneumatic soft bionic robot based on improved CPG.

To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.

A Wild Horse Optimization algorithm with chaotic inertia weights and its application in linear antenna array synthesis.

Antennas play a crucial role in designing an efficient communication system. However, reducing the maximum sidelobe level (SLL) of the beam pattern is a crucial challenge in antenna arrays. Pattern synthesis in smart antennas is a major area of research because of its widespread application across various radar and communication systems. This paper presents an effective technique to minimize the SLL and thus improve the radiation pattern of the linear antenna array (LAA) using the chaotic inertia-weighted Wild Horse optimization (IERWHO) algorithm. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. The IERWHO algorithm is an improved Wild Horse optimization (WHO) algorithm that combines the concepts of chaotic sequence factor, nonlinear factor, and inertia weights factor. In this paper, the method is applied for the first time in antenna array synthesis by optimizing parameters such as inter-element spacing and excitation to minimize the SLL while keeping other constraints within the boundary limits, while ensuring that the performance is not affected. For performance evaluation, the simulation tests include 12 benchmark test functions and 12 test functions to verify the effectiveness of the improvement strategies. According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.

Transmural APD heterogeneity determines ventricular arrhythmogenesis in LQT8 syndrome: Insights from Bidomain computational modeling.

Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. While prior studies hint at CACNA1C mutations' role in ventricular arrhythmia genesis, the mechanisms, especially in G406R presence, are not fully understood. This computational study explores how the G406R mutation, causing increased transmural dispersion of repolarization, induces and sustains reentrant ventricular arrhythmias. Using three-dimensional numerical simulations on an idealized left-ventricular model, integrating the Bidomain equations with the ten Tusscher-Panfilov ionic model, we observe that G406R mutation with 11% and 50% heterozygosis significantly increases transmural dispersion of repolarization. During S1-S4 stimulation protocols, these gradients facilitate conduction blocks, triggering reentrant ventricular tachycardia. Sustained reentry pathways occur only with G406R mutation at 50% heterozygosis, while neglecting transmural heterogeneities of action potential duration prevents stable reentry, regardless of G406R mutation presence.

Extreme value statistics of nerve transmission delay.

Delays in nerve transmission are an important topic in the field of neuroscience. Spike signals fired or received by the dendrites of a neuron travel from the axon to a presynaptic cell. The spike signal then triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, regenerates electrical signals via a chemical reaction through ion channels, and transmits them to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme-order statistics. By considering the statistical variance in the time constant of the leaky Integrate-and-Fire model, a deterministic time evolution model for spike signals, we enabled randomness in the time interval of the spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, one of the three forms of extreme-value statistics. We further confirmed that the histogram of the maximum time interval followed a Fréchet distribution when the time interval of the spike signal followed a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and can therefore be used as a new indicator of transmission delay.