Computational Methods Research Articles

Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients.

Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch. Hemodynamic characteristics were estimated in key subdomains of the aorta and three branches using computational fluid dynamics methods. The key morphometric features (diameters) were calculated at locations in proximity to the domains, where hemodynamic characteristics are evaluated. Results: The functional dependencies for velocities and pressure on the corresponding diameters have been fitted, and a metamodel has been constructed employing the predicted values from these models. Conclusions: The metamodel demonstrated high accuracy in classifying aortas into their respective types, thereby confirming the adequacy of the predicted hemodynamic characteristics by morphometric characteristics. The proposed methodology is applicable to other heart diseases without fundamental changes.

A novel procedure for selection of molecular descriptors: QSAR model for mutagenicity of nitroaromatic compounds.

Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity.

Snapshot computational spectroscopy enabled by deep learning

Abstract Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios.

A high-throughput statistical homogenization technique to convert realistic microstructures into idealized periodic unit cells

Abstract Metal alloys frequently contain distributions of second-phase particles that deleteriously affect the material
behavior by acting as sites for void nucleation. These distributions are often extremely complex and
processing can induce high levels of anisotropy. The particle length-scale precludes high-fidelity microstructure
modeling in macroscale simulations, so computational homogenization methods are often employed.
These, however, involve simplifying assumptions to make the problem tractable and many rely on periodic
microstructures. Here we propose a methodology to bridge the gap between realistic microstructures
composed of anisotropic, spatially varying second-phase void morphologies and an idealized periodic microstructure
with roughly equivalent mechanical response. We create a high-throughput, parametric study
to investigate 96 unique bridging methods. We apply our proposed solution to rolled AZ31B magnesium
alloy, for which we have a rich dataset of microstructure morphology and mechanical behavior. Our methodology
converts a μ-CT scan of the realistic microstructure to idealized periodic unit cell microstructures that
are specific to the loading orientation. We recreate the unit cells for each parameter set in a commercial finite
element software, subject them to macroscopic uniaxial loading conditions, and compare our results to the
datasets for the various loading orientations. We find that certain combinations of our parameters capture
the overall stress-strain response, including anisotropy effects with some degree of success. The effect of
different parameter options are explored in detail and we find that excluding certain particle populations
from the analysis can give improved results.

Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins

Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins

A novel compartmental approach for modeling stomach motility and gastric emptying

The stomach, a central organ in the Gastrointestinal (GI) tract, regulates the processing of ingested food through gastric motility and emptying. Understanding the stomach function is crucial for treating gastric disorders. Experimental studies in this field often face difficulties due to limitations and invasiveness of available techniques and ethical concerns. To counter this, researchers resort to computational and numerical methods. However, existing computational studies often isolate one aspect of the stomach function while neglecting the rest and employ computationally expensive methods. This paper proposes a novel cost-efficient multi-compartmental model, offering a comprehensive insight into gastric function at an organ level, thus presenting a promising alternative.The proposed approach divides the spatial geometry of the stomach into four compartments: Proximal/Middle/Terminal antrum and Pyloric sphincter. Each compartment is characterized by a set of ordinary differential equations (ODEs) with respect to time to characterize the stomach function. Electrophysiology is represented by simplified equations reflecting the “slow wave behavior” of Interstitial Cells of Cajal (ICC) and Smooth Muscle Cells (SMC) in the stomach wall. An electro-mechanical coupling model translates SMC “slow waves” into smooth muscle contractions. Muscle contractions induce peristalsis, affecting gastric fluid flow velocity and subsequent emptying when the pyloric sphincter is open. Contraction of the pyloric sphincter initiates a retrograde flow jet at the terminal antrum, modeled by a circular liquid jet flow equation.The results from the proposed model for a healthy human stomach were compared with experimental and computational studies on electrophysiology, muscle tissue mechanics, and fluid behavior during gastric emptying. These findings revealed that each “ICC” slow wave corresponded to a muscle contraction due to electro-mechanical coupling behavior. The rate of gastric emptying and mixing efficiency decreased with increasing viscosity of gastric liquid but remained relatively unchanged with gastric liquid density variations. Utilizing different ODE solvers in MATLAB, the model was solved, with ode15s demonstrating the fastest computation time, simulating 180 s of real-time stomach response in just 2.7 s. This multi-compartmental model signifies a promising advancement in understanding gastric function, providing a cost-effective and comprehensive approach to study complex interactions within the stomach and test innovative therapies like neuromodulation for treating gastric disorders.

EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis.

The integrity of the reconstructed human epidermis generated in vitro can be assessed using histological analyses combined with immunohistochemical staining of keratinocyte differentiation markers. Technical differences during the preparation and capture of stained images may influence the outcome of computational methods. Due to the specific nature of the analyzed material, no annotated datasets or dedicated methods are publicly available. Using a dataset with 598 unannotated images showing cross-sections of in vitro reconstructed human epidermis stained with DAB-based immunohistochemistry reaction to visualize four different keratinocyte differentiation marker proteins (filaggrin, keratin 10, Ki67, HSPA2) and counterstained with hematoxylin, we developed an unsupervised method for the detection and quantification of immunohistochemical staining. The pipeline consists of the following steps: (i) color normalization; (ii) color deconvolution; (iii) morphological operations; (iv) automatic image rotation; and (v) clustering. The most effective combination of methods includes (i) Reinhard's normalization; (ii) Ruifrok and Johnston color-deconvolution method; (iii) proposed image-rotation method based on boundary distribution of image intensity; and (iv) k-means clustering. The results of the work should enhance the performance of quantitative analyses of protein markers in reconstructed human epidermis samples and enable the comparison of their spatial distribution between different experimental conditions.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.

Correction to “A Comprehensive Analysis of Electronic Transitions in Naphthalene and Perylene Diimide Derivatives Through Computational Methods”

Correction to “A Comprehensive Analysis of Electronic Transitions in Naphthalene and Perylene Diimide Derivatives Through Computational Methods”

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Research on the mechanism of motor muscle control based on optical EEG images

The study of motor muscle control mechanisms can improve rehabilitation therapy and human-computer interaction technology. The limitations of traditional electroencephalography (EEG) limit the comprehensive understanding of motor muscle control mechanisms. Therefore, this study aims to explore the mechanism of motor muscle control based on optical EEG images, in order to expand the understanding of the process of motor control. The study selected optical EEG imaging technology as the main data acquisition tool. Optical EEG images have higher spatiotemporal resolution and can provide more detailed neural activity information. This technology combines optical imaging with EEG images to obtain spatiotemporal information of brain activity in a short period of time. The device is composed of multiple optical sensors and can measure blood oxygen concentration and neuronal activity in the cerebral cortex. Preprocess EEG image data using image processing and signal processing techniques, then use computational methods and algorithms to detect activated regions, and evaluate their relationships using correlation analysis and statistical methods. By comparing EEG image data and motor muscle activity data under different motor tasks. The research results show that optical EEG imaging technology can provide more detailed information on brain neural activity and accurately capture the activity patterns of different motor muscles. These results provide new perspectives and methods for further studying the control mechanisms of motor muscles.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

Exploring the Logic and Conducting a Comprehensive Evaluation of the Adiponectin Receptor Agonists AdipoRon and AdipoAI's Impacts on Bone Metabolism and Repair-A Systematic Review.

Adiponectin replacement therapy shows promising outcomes in various diseases, especially for bone-related disorders. Challenges in using the complete protein have led to alternative approaches, with AdipoRon and AdipoAI emerging as extensively researched drug candidates. Their influence on models of bone-related disorders has progressed considerably but there has been no review of their effectiveness in modulating bone metabolism and repair. This systematic review seeks to address this knowledge gap. Based on preclinical evidence from PubMed, EMBASE, and COCHRANE, ten studies were included following PRISMA guidelines. The JBI Checklist Critical Appraisal Tool assessed the quality of this systematic review. The studies encompassed various animal models, addressing bone defects, osseointegration, diabetes-associated periodontitis, fracture repair, growth retardation, and diabetes-associated peri-implantitis. AdipoRon and AdipoAI demonstrated effectiveness in modulating bone metabolism and repair through diverse pathways, including the activation of AdipoR1/APPL1, inhibition of F-actin ring formation, suppression of IκB-α phosphorylation, p65 nuclear translocation and Wnt5a-Ror2 signaling pathway, reduction of CCL2 secretion and expression, regulation of autophagy via LC3A/B expression, modulation of SDF-1 production, activation of the ERK-1/2 signaling pathway, modulation of bone integration-related markers and osteokines such as RANKL, BMP-2, OPG, OPN, and Runx2, inhibition of RANKL in osteoblasts, and inhibition of podosome formation via the activation of AMPK. While preclinical studies show promise, human trials are crucial to confirm the clinical safety and effectiveness of AdipoRon and AdipoAI. Caution is necessary due to potential off-target effects, especially in bone therapy with multi-target approaches. Structural biology and computational methods can help predict and understand these effects.

Transport and clogging dynamics of flexible rods in pore constrictions.

The transport and clogging behavior of flexible particles in confined flows is a complex interplay between elastic and hydrodynamic forces and wall interactions. While the motion of non-spherical particles in unbounded flows is well understood, their behavior in confined spaces remains less explored. This study introduces a coupled computational fluid dynamics-discrete element method (CFD-DEM) approach to investigate the transport and clogging dynamics of flexible rod-shaped particles in confined pore constrictions. The spatio-temporal analysis reveals the influence of the rod's initial conditions and flexibility on its transport dynamics through a pore constriction. The simulation results demonstrate an increase in the lateral drift of the rod upon exiting the pore that can be scaled with channel height confinement. The clogging dynamics are explored based on hydrodynamic and mechanical forces, unveiling conditions for mechanical clogging through sieving. The developed method allows for the deconvolution of the forces that contribute to particle trajectories in confined flow, which is highly relevant in particle separation processes, fibrous-shaped virus filtration, biological flows, and related applications. The method is embedded into the open-source CFDEM framework, facilitating future extensions to explore multiple particle dynamics, intermolecular forces, external influences, and complex geometries.

A spring-mass-damper model based on separated phase flow mode for pulsating heat pipe with adjustive-structured channels

Pulsating heat pipe (PHP) is a kind of efficient passive phase-change cooling device. The pulsating behaviors of the two-phase flow inside PHP significantly affect the heat transfer performance for PHP, the investigation of which will greatly contribute to the optimal design of PHP for electronic heat dissipation in small space. In the present work, the heat transfer performance of PHP is optimized via structure analysis and modeling calculation. A new “spring-mass-damper” model in terms of separated phase flow mode is established, where the frictional pressure loss of the real two-phase flow pattern − slug flow in PHP is considered. Besides, a prototype of PHP with adjustive-structured channel (ASCPHP) is proposed. The heat transfer performance of ASCPHP is evaluated with the newly established model. With theoretical computation method, the frequency of ASCPHP the superiority of ASPHP is also confirmed by comparison with other types of PHPs.

What I was thinking/what I would do differently: Biological markers and mechanisms of mental health.

At the 39th meeting of the International Society of Traumatic Stress Studies, four leading scientists and clinicians were invited to reflect on their careers, focusing on the biological mechanisms and markers of traumatic stress. Dr. Raul Andero has contributed to understanding how stress alters memory networks in the brain, influencing the development of novel treatments. Dr. Tanja Jovanovic has pioneered the measurement and mechanistic understanding of fear learning, bridging basic and clinical research. Dr. Murray B. Stein has scaled up clinical and lab observations to large populations, refining the field's understanding of traumatic stress. Dr. Arieh Y Shalev has shaped the definition of traumatic stress, pioneering the longitudinal investigation of stress and integrating advanced computational methods to identify individuals at risk. These panelists were asked to reflect on their initial problems, ambitions, concerns, and unexpected challenges, as well as the influence of their work, on new research trajectories. Their insights provide valuable lessons about the process and content of their work, and their pioneering efforts have significantly advanced our understanding of the biological mechanisms and markers of traumatic stress.

Using multiple, dynamically linked representations to develop representational competency and conceptual understanding of the earthquake cycle

Using computational methods to produce and interpret multiple scientific representations is now a common practice in many science disciplines. Research has shown students have difficulty in moving across, connecting, and sensemaking from multiple representations. There is a need to develop task-specific representational competencies for students to reason and conduct scientific investigations using multiple representations. In this study, we focus on three representational competencies: 1) linking between representations, 2) disciplinary sensemaking from multiple representations, and 3) conceptualizing domain-relevant content derived from multiple representations. We developed a block code-based computational modeling environment with three different representations and embedded it within an online activity for students to carry out investigations around the earthquake cycle. The three representations include a procedural representation of block codes, a geometric representation of land deformation build-up, and a graphical representation of deformation build-up over time. We examined the extent of students' representational competencies and which competencies are most correlated with students’ future performance in a computationally supported geoscience investigation. Results indicate that a majority of the 431 students showed at least some form of representational competence. However, a relatively small number of students showed sophisticated levels of linking, sensemaking, and conceptualizing from the representations. Five of seven representational competencies, the most prominent being code sensemaking (η2 = 0.053, p < 0.001), were significantly correlated to student performance on a summative geoscience investigation.

FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-art to Future Opportunities

Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing applications, such as deep learning, image processing, and physical simulation. Field-Programmable Gate Arrays (FPGAs), with their configurable hardware resources, can be tailored to accelerate SpMMs. There has been considerable research on deploying sparse matrix multipliers across various FPGA platforms. However, the FPGA-based design of sparse matrix multipliers still presents numerous challenges. Therefore, it is necessary to summarize and organize the current work to provide a reference for further research. This paper first introduces the computational method of SpMM, and categorizes the different challenges of FPGA deployment. Following this, we introduce and analyze a variety of state-of-the-art FPGA-based accelerators tailored for SpMMs. In addition, a comparative analysis of these accelerators is performed, examining metrics including compression rate, throughput, and resource utilization. Finally, we propose potential research directions and challenges for further study of FPGA-based SpMM acclerators.

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies

BackgroundThis study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally. MethodsData from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance. ResultsKey predictors for CJCC adolescents’ sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents. ConclusionThe identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activity via experimental approaches. To partially address this issue, several computational advances have been proposed. In this study, we present iACP-GCR, a model based on multitask learning on graph convolutional residual neural networks with two types of shortcut connections, to identify multitarget anticancer compounds. In our architecture, the graph convolutional residual neural networks are shared by all the prediction tasks before being separately customized. The NCI-60 data set, one of the most reliable and well-known sources of experimentally verified compounds, was used to develop our model. From that data set, we collected and refined data about compounds screened across nine cancer types (panels), including breast, central nervous system, colon, leukemia, nonsmall cell lung, melanoma, ovarian, prostate, and renal, for model training and evaluation. The model performance evaluated on an independent test set shows that iACP-GCR surpasses the three advanced computational methods for multitask learning. The integration of two shortcut connection types in the shared networks also improves the prediction efficiency. We also deployed the model as a public web server to assist the research community in screening potential anticancer compounds.