Accurate Model Research Articles

An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.

Exploring fractional-order new coupled Korteweg-de Vries system via improved Adomian decomposition method.

This paper aims to extend the applications of the projected fractional improved Adomian Decomposition method (fIADM) to the fractional order new coupled Korteweg-de Vries (cKdV) system. This technique is significantly recognized for its effectiveness in addressing nonlinearities and iteratively handling fractional derivatives. The approximate solutions of the fractional-order new cKdV system are obtained by employing the improved ADM in fractional form. These solutions play a crucial role in designing and optimizing systems in engineering applications where accurate modeling of wave phenomena is essential, including fluid dynamics, plasma physics, nonlinear optics, and other mathematical physics domains. The fractional order new cKdV system, integrating fractional calculus, enhances accuracy in modeling wave interactions compared to the classical cKdV system. Comparison with exact solutions demonstrates the high accuracy and ease of application of the projected method. This proposed technique proves influential in resolving fractional coupled systems encountered in various fields, including engineering and physics. Numerical results obtained using Mathematica software further verify and demonstrate its efficacy.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis.

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS. Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%). The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain. A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.

Ab initio Molecular Dynamics Investigation on the Permeation of Sodium and Chloride Ions through Nanopores in Graphene and Hexagonal Boron Nitride Membranes.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car-Parrinello molecular dynamics simulations to establish an accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. We demonstrate that ion separation works well for the h-BN system by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation, respectively. In contrast, for permation of the graphene nanopore, the Cl- ion faces a minimum of energy of 0.68 eV in the nanopore plane and is prone toward blockade of the nanopore, while the Na+ ion experiences a slight minimum of 0.03 eV. Overall, the desalination performance of h-BN nanopores surpasses that of their graphene counterparts.

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques—random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines—are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users’ convenience. Doi: 10.28991/CEJ-SP2024-010-01 Full Text: PDF

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

An accurate and realistic polarization model for night-sky brightness

Abstract Most measurements of the diffuse light of the night sky to date consider only the intensity of the light field, and current models can successfully reproduce these measurements. However, this approach is incomplete as it overlooks the polarization state of the light. Few measurements (and no successful models) of night-sky polarimetry appear in the literature. We present a new model of night-sky polarization that successfully reproduces observations in a heterogeneous environment and for a real distribution of finite-sized light sources over the intermediate region surrounding the observer. The model compares favourably with field measurements made in Slovakia in May 2021. The results described herein help advance understanding the angular distributions of artificial light at night from ground sources and the relative contributions of sources to the overall brightness of the night sky.

Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study.

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.

Construction and validation of nomogram prognostic model for predicting survival in hepatoblastoma patients: a population-based study.

For patients with hepatoblastoma (HB), current staging system is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. A retrospective analysis of 424 HB patients was performed from 2004 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to screen for variables. The identified variables were used to build survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, and receiver operating characteristic (ROC) curve. The Cox regression analysis identified six variables affecting overall survival (OS) in HB patients, including race, tumor size, lymph node involvement, distant metastases, surgery and chemotherapy. And the Cox regression analysis identified five variables including race, lymph node involvement, distant metastases, surgery, and chemotherapy that affect cancer-specific survival (CCS) in HB patients. In the training cohort, the C-index of the nomogram in predicting the OS was 0.791 [95% confidence intervals (95% CI) 0.717-0.865], CSS was 0.805(95% CI 0.728-0.882). In the validation cohort, the C-index of the nomogram in predicting the OS was 0.712 (95% CI 0.511-0.913), the CSS was 0.751 (95% CI 0.566-0.936). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1-, 3-, and 5-year OS were 0.842 (95% CI 0.739-0.944), 0.759 (95% CI 0.670-0.849), and 0.770 (95% CI 0.686-0.852), respectively. In the validation cohort, the AUC values for prediction of the 1-, 3-, and 5-year OS were 0.920 (95% CI 0.806-1.034), 0.863 (95% CI 0.750-0.976), and 0.844 (95% CI 0.721-0.967), respectively. Two nomogram models were developed and validated in this study which provided accurate prediction of the OS and CSS in HB patients. The constructed models can be used for predicting survival outcomes and guide treatment for HB patients.

BPNS: A Fast and Accurate Power Estimation Model for Embedded Devices

The surge in the intelligent information age has led to a growing need for enhanced computing power in smart mobile devices. However, the accompanying increase in computing power brings about a significant challenge — high power consumption. This poses a notable hurdle for mobile devices that prioritize sustainability performance. Effectively addressing power consumption estimation is crucial for overcoming this sustainability issue. In this paper, we propose using a branching parallel neural networks (BPNS) that consists of FCNN-LSTM and a attention mechanism to estimate the power consumption of a device. In addition, a data stabilization module is incorporated into the framework to improve the power estimation accuracy. We implement and assess the performance of BPNS for online estimation across diverse smart mobile devices, conducting a comparative analysis with commonly used neural networks. Notably, our approach achieves a deviation of 0.28W, 0.65W and 0.76W for online estimation on three smart mobile devices, and outperforms other methods in terms of estimation accuracy and computational cost.

Molecular dynamics simulations shed light into the donor substrate specificity of vertebrate poly-alpha-2,8-sialyltransferases ST8Sia IV

BackgroundSialic acids are essential monosaccharides influencing several biological processes and disease states. The sialyltransferases catalyze the transfer of Sia residues to glycoconjugates playing critical roles in cellular recognition and signaling. Despite their importance, the molecular mechanisms underlying their substrate specificity, especially between different organisms, remain poorly understood. Recently, the human ST8Sia IV, a key enzyme in the synthesis of polysialic acids, was found to accept only CMP-Neu5Ac as a sugar-donor, whereas the whitefish Coregonus maraena enzyme showed a wider donor substrate specificity, accepting CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn. However, what causes these differences in donor substrate specificity is unknown. MethodsComputational approaches were used to investigate the structural and biochemical determinants of the donor substrate specificity in ST8Sia IV. Accurate structural models of the human and fish ST8Sia IV catalytic domains and their complexes with three sialic acid donors (CMP-Neu5Ac, CMP-Neu5Gc, and CMP-Kdn) were generated. Subsequently, molecular dynamics simulations were conducted to analyze the stability and interactions within these complexes and identify differences in complex stability and substrate binding sites between the two ST8Sia IV. ResultsOur MD simulations revealed that the human enzyme effectively stabilizes CMP-Neu5Ac, whereas CMP-Neu5Gc and CMP-Kdn are unstable and explore different conformations. In contrast, the fish ST8Sia IV stabilizes all three donor substrates. Based on these data, we identified the key interacting residues for the different Sias parts of the substrate donors. General significanceThis work advances our knowledge of the enzymatic mechanisms governing sialic acid transfer, shedding light on the evolutionary adaptations of sialyltransferases.

Oropharyngeal swallowing hydrodynamics of thin and mildly thick liquids in an anatomically accurate throat-epiglottis model

Understanding the mechanisms underlying dysphagia is crucial in devising effective, etiology-centered interventions. However, current clinical assessment and treatment of dysphagia are still more symptom-focused due to our limited understanding of the sophisticated symptom-etiology associations causing swallowing disorders. This study aimed to elucidate the mechanisms giving rise to penetration flows into the laryngeal vestibule that results in aspirations with varying symptoms. Methods: Anatomically accurate, transparent throat models were prepared with a 45° down flapped epiglottis to simulate the instant of laryngeal closure during swallowing. Fluid bolus dynamics were visualized with fluorescent dye from lateral, rear, front, and endoscopic directions to capture key hydrodynamic features leading to aspiration. Three influencing factors, fluid consistency, liquid dispensing site, and dispensing speed, were systemically evaluated on their roles in liquid aspirations. Results: Three aspiration mechanisms were identified, with liquid bolus entering the airway through (a) the interarytenoid notch (notch overflow), (b) cuneiform tubercle recesses (recess overflow), and (c) off-edge flow underneath the epiglottis (off-edge capillary flow). Of the three factors considered, liquid viscosity has the most significant impact on aspiration rate, followed by the liquid dispensing site and the dispensing speed. Water had one order of magnitude higher aspiration risks than 1% w/v methyl cellulose solution, a mildly thick liquid. Anterior dispensing had higher chances for aspiration than posterior oropharyngeal dispensing for both liquids and dispensing speeds considered. The effects of dispending speed varied. A lower speed increased aspiration for anterior-dispensed liquids due to increased off-edge capillary flows, while it significantly reduced aspiration for posterior-dispensed liquids due to reduced notch overflows. Visualizing swallowing hydrodynamics from multiple orientations facilitates detailed site-specific inspections of aspiration mechanisms.

Community Detection Using Modularity Optimization Method For Catchment Classification

A general framework for catchment classification may be helpful for more accurate and efficient modeling of hydrologic systems, as well as to improve communication between hydrology researchers and those in other disciplines. There are plethora numbers of methods applied for catchment classification, but in these years, recent studies are implementing the complex networks concept for classification purposes. The community structure methods which are complex networks-based methods are focus mainly to classify catchments. Hence, the efficiency of complex network ideas, especially using the methods of community structure is examined in this study. Specifically, the modularity optimization method that is one of the community structure methods is applied to classify 218 stream-gauges stations in entire Australia that covers a large variety of hydroclimatic, topographic, geomorphic, soil usage, and climatic parameters. In the present study, the applicability and the efficiency of the community structure concept is validated by the proposed method. The classification of Australian catchments was further assessed with threshold value of 0.8, which resulted formation of nine communities with at least 9 stations in a community which combine to have almost 77% of the total number of stations (165 out of 218). All nine selected communities were also examined in terms of the flow characteristics (i.e. flow mean and flow covariance) and the catchment characteristics (i.e. drainage area, elevation and stream length). The catchment behaviors for each selected communities were also interpreted in terms of distance and correlation relationship, which give some useful insights towards generalization of hydrologic framework.

Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans.

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

Analysis of the acoustoelectric response of SAW gas sensors using a COM model

Surface acoustic wave (SAW) gas sensors based on the acoustoelectric effect exhibit wide application prospects for in situ gas detection. However, establishing accurate models for calculating the scattering parameters of SAW gas sensors remains a challenge. Here, we present a coupling of modes (COM) model that includes the acoustoelectric effect and specifically explains the nonmonotonic variation in the center frequency with respect to the sensing film’s sheet conductivity. Several sensing parameters of the gas sensors, including the center frequency, insertion loss, and phase, were experimentally compared for accuracy and practicality. Finally, the frequency of the phase extremum (FPE) shift was determined to vary monotonically, and the range of selectable test points was wide, making the FPE an appropriate response parameter for leveraging in SAW gas sensors. The simulation results of the COM model were highly consistent with the experimental results. Our study is proposed to provide theoretical guidance for the future development of gas SAW sensors.

Modeling heat and mass transfer in granular flows between vertical parallel plates

Gravity-driven granular flows between vertical parallel plates at elevated temperatures are integral in the development of the next generation of particle-based heat exchangers for concentrated solar power. Efficient modeling methodologies that accurately captures the heat transfer mechanisms of temperature-dependent granular flows are essential to effectively design and evaluate these heat exchangers. Transient, pseudo-one-dimensional heat and mass transfer models were developed for inlet flow temperatures between 473 and 1073 K. The mass transfer models for the granular flows were resolved using discrete element method simulation, providing detailed temporal and spatial distributions of flow parameters in good agreement with experimental measurements. The heat transfer model employed a one-dimensional, two-phase, continuum approach, and a system of non-linear differential equations was solved via finite difference method. The particle-to-wall contact heat transfer was determined with an effective thermal conductance model. Radiative heat transfer between particles and walls was also considered. The predicted particle and wall temperatures for two different channel widths of 6.4 and 2 mm were well-correlated to previously obtained experimental measurements with Pearson’s correlation coefficients greater than 0.91. A significant disparity in particle temperature of up to 72 K was observed when radiative heat transfer was omitted, and a maximum temperature difference of 11 K was observed when temperature-dependent granular flow properties were not considered. The model revealed that radiative heat transfer potentially contributed to > 30 % of heat losses in wider channels at high inlet temperatures, and conduction was the dominated heat transfer mechanisms in narrower channels (∼70 %) due to increased particle-to-wall contact. This accurate model, leveraging discrete element method in a streamlined approach, advances the understanding and modeling of heat transfer in granular flows at elevated temperatures, enabling more efficient and reliable solar thermal energy storage and transport.

DC3DPAFEM: An efficient and accurate 3-D direct current resistivity anisotropic forward modeling software for complex geological settings

Nowadays, there is a growing trend that direct current (DC) field surveys are shifting towards challenging areas characterized by mountainous topography and electrical anisotropy. Given these complex geological settings, there is an urgent need for 3-D DC forward modeling software capable of effectively addressing large-scale problems and delivering accurate modeling results to interpret field data. However, most open-source software packages face certain limitations, such as the high numerical cost to handle complex surface topography, the lack of consideration for anisotropic conductivity, the absence of mesh refinement techniques to guarantee accuracy in forward modeling, and the lack of parallel computing techniques to solve large-scale problems. In this study, we develop an efficient and highly accurate 3-D DC anisotropic forward modeling software, namely DC3DPAFEM, using the adaptive finite element algorithm based on the unstructured tetrahedral mesh. Firstly, we construct a strong compatible boundary value problem (BVP) for 3-D anisotropic DC problems by adopting a specialized secondary potential approach to handle the surface topography efficiently. Then, we develop a goal-oriented adaptive mesh refinement (AMR) technique to ensure accurate forward modeling results, even with a coarse initial mesh. To ensure time and memory efficiency, we employ a robust conjugate gradient (CG) algorithm preconditioned by the algebraic multigrid (AMG) solver to solve the large-scale linear system of equations resulting from complex geological structures. We aim to investigate the performance of the AMG scheme in anisotropic DC cases. Furthermore, we incorporate the domain decomposition technique into the iterative solution scheme for further efficiency gains. This technique significantly improves computing efficiency for large-scale problems in parallel clusters. Finally, we conduct comprehensive performance tests for DC3DPAFEM using a two-layer anisotropic model and a 3-D complex model with undulating terrain. The results of both examples validate the accuracy of DC3DPAFEM, as they closely align with the analytical solutions and the solutions obtained from the existing 3-D DC forward modeling code. Compared to traditional direct solver MUMPS and ILU-preconditioned iterative solvers, DC3DPAFEM exhibits highly scalable performance for large-scale problems, offering significant advantages in terms of memory and time consumption. Overall, DC3DPAFEM demonstrates substantial advances in efficiency, accuracy, and practicality through a series of numerical examples. This open-source code provides an efficient and available tool for developing a 3-D DC inversion method that can deal with large-scale problems involving intricate topography and anisotropic media.

Carbon Dioxide Capturing in Temperature/Pressure Swing‐Commercial Processes Using Ionic Liquids

AbstractCapturing carbon dioxide is vital for mitigating global warming and supporting chemical processes. Ionic liquids (ILs) have emerged as promising solvents for CO2 capture. Using ASPEN simulation software, this study explores three specific ILs: [emim][triflate], [bmim][MeSO3], and [bmim][NTf2]. Their selection is based on exploitable differences in energy. The study models CO2 solubility and validates it against published data. It also considers the decomposition of ILs using a more accurate vapor loss model. Simulated equations predict CO2 and IL behavior more accurately than published ASPEN studies, optimizing the process for minimal energy consumption. Commercial considerations and rigorous engineering calculations guide the analysis, encompassing energy and economic factors.

Mathematical modeling of biomechanical elastic and hyperelastic properties of the myocardium

BACKGROUND: The study of mechanical properties of biological tissues is extremely informative and is one of the most important areas of biomechanics. Knowledge of these aspects of biological objects based on experimental data can become a source of new medical and technical solutions for the reconstruction of organs and the development of replacement materials. AIM: Passive mechanical properties of isolated myocardium are compared with linear, bilinear, exponential and the most common hyperelastic models (neohookean, Mooney–Rivlin, Ogden, Yeoh, polynomial and Veronda–Westmann). MATERIALS AND METHODS: Literature data on mechanical tests of autopsy material obtained from mongrel dogs were used as initial data. To search for the most advanced calculation algorithms the computer algebra system was used, the Mathcad 15.0 software package and the multifunctional finite element analysis application ANSYS 2022 R2 were used. Direct comparison of models was made based on mathematical statistics. RESULTS: Among the first group of models, the results closest to the experimental data were demonstrated by the exponential model R = 0.9958/0.9984 (in the longitudinal/transverse direction with respect to the myocardial fibers), the lowest accuracy was demonstrated by the linear model R = 0.9813/0.9803. Young’s moduli of linear, bilinear and exponential models and material constants of hyperelastic models are determined. The coefficient of elastic anisotropy of the myocardium, defined as the ratio of the elastic moduli of the linear model measured along and across the direction of the fibers, is equal to 2.18, which is very different from the literature data for the myocardium of the human heart. Deformation along the fibers of the heart muscle is more energy-consuming in the direction along the fibers than in the transverse direction (3.81 and 2.52 mJ/cm3). The most accurate hyperelastic models turned out to be the 2nd order polynomial model R = 0.9971 and the 3rd order Yeoh model R = 0.997. The largest deviations and the lowest correlation coefficient between the experimental and model data were demonstrated by the simple neohookean model R = 0.974 with a single parameter μ. The numerical values of the parameters of hyperelastic models obtained by calculation methods used practically did not differ from each other (≤2.16%). CONCLUSIONS: The study demonstrated the importance of selecting the correct mechanical model for isolated myocardium. The data obtained can be useful in virtual interventions (simulations) for predicting outcomes and supporting clinical decisions, developing replacement materials and structures made of them for reconstructive operations on heart structures.

The mechanics of cilia and flagella: What we know and what we need to know.

In this review, we provide a condensed overview of what is currently known about the mechanical functioning of the flagellar/ciliary axoneme. We also present a list of 10 specific areas where our current knowledge is incomplete and explain the benefits of further experimental investigation. Many of the physical parameters of the axoneme and its component parts have not been determined. This limits our ability to understand how the axoneme structure contributes to its functioning in several regards. It restricts our ability to understand how the mechanics of the structure contribute to the regulation of motor function. It also confines our ability to understand the three-dimensional workings of the axoneme and how various beating modes are accomplished. Lastly, it prevents accurate computational modeling of the axoneme in three-dimensions.