Advanced Computational Tools Research Articles

The impact of aging and atrial fibrillation on thrombus formation in-silico

Abstract Background Atrial fibrillation (AF) is an age-related cardiac arrythmia that underlies one third of all strokes, with thromboemboli commonly originating from the left atrial appendage (LAA). Personalised in-silico modelling of Virchow’s triad (blood stasis, hypercoagulability and endothelial damage) can improve stroke risk assessment metrics, such as the CHA2DS2-VASc score, by providing quantitative mechanistic information [1]. The ability to quantify the impact of aging may play a critical role in patient stratification and therapy selection to reduce the risk of AF-related stroke. Aim We aim to perform in-silico modelling to quantify the effect of aging and AF on thrombus formation in the LAA by using advanced computational tools to capture blood flow, clotting mechanisms and endothelial damage [2]. Methods A virtual physiological LA model was derived from cardiac Cine MRI from an adult AF patient imaged during sinus rhythm (SR). Biomarkers for a geriatric patient were obtained from literature and applied as: i) elevated plasma fibrinogen concentration and ii) longer thrombin time-to-peak (ttP) [3]. Blood flow was simulated in SR and AF conditions for 5 cardiac cycles in the LA for both ages (totalling 4 test cases). The catalytic conversion of fibrinogen to a fibrin clot by thrombin was modelled to mimic hypercoagulability [2]. The site of endothelial injury was prescribed in the middle of the LAA. The area under curve for the fibrin concentration (Fn AUC) was measured to quantify thrombus formation. Results In the two SR cases, LA blood flow velocity increased to a maximum of 1.8 m/s after 0.6 s with a mean of 0.63 m/s during the cardiac cycle, while the two AF cases showed 1.3 m/s and 0.39 m/s, respectively, corresponding to a 38% decrease in AF. Peak velocity in the LAA was 0.35 m/s during SR and 0.18 m/s in AF. The fibrin concentration in the adult-SR case rose to 1.1 mmol/m³ over the first cardiac cycle, but both thrombin and fibrin were washed out of the LA after 1.5 s, with a Fn AUC of 0.045mmol s/m³ (Fig. 1a). The geriatric-SR case sustained fibrin generation throughout the 5 cardiac cycles reaching 3.1 mmol/m³, an Fn AUC of 2.4 mmol s/m³, and the formation of an LAA thrombus (Fig. 1b). Similarly, the adult-AF and geriatric-AF cases also saw continuous growth to 5.0 and 7.4 mmol/m³, with Fn AUCs of 6.4 and 12.6 mmol s/m³, respectively, with formation of a thrombus in the LAA in both (Fig. 1c and d). The thrombus formed in the adult-AF case was 25% larger than the geriatric-SR case, while thrombus in the geriatric-AF case was 80% larger. Conclusions This investigation quantifies the increased risk of LAA thrombus formation in older patients, with an Fn AUC increase of 55% between adult and geriatric in SR and a 100% increase when combining geriatric biomarkers with AF conditions. With further validation on larger patient cohorts, this novel in-silico tool may improve our understanding of the risks in AF-related stroke.

Molecular dynamics calculations of the enthalpy of vaporization for different water models

Phase-change heat transfer processes involving water play a key role in numerous societal applications, such as electrical power generation, microelectronics cooling, or water desalination. Advanced computational tools, such as molecular dynamics (MD), are increasingly being used to simulate these processes, especially on nanostructured surfaces and in nanoparticle suspensions, to gain insight into the unique nanoscale physics at play. In these studies, it is critical to use models of the water molecule that yield accurate predictions of water’s thermophysical properties, particularly its enthalpy of vaporization. Here, we use MD to determine the enthalpy of vaporization (hfg) for six common water models and four different values of the cutoff radius (i.e., the intermolecular distance beyond which interactions are simplified or ignored). The most accurate prediction of hfg (within 2 % of the value tabulated by ASHRAE) comes from the TIP3P water model with a cutoff radius of 14 Å. We also conducted a preliminary study of the variation of hfg with pressure and found reasonable agreement between simulations and tabulated ASHRAE data (<7% error). This work provides physical insights into the sensitivity of the predicted macroscopic thermophysical properties of water to minute changes in the atomic-level properties and is expected to inform future MD-based studies of phase change processes of water, especially as they occur at elevated pressures and on nanostructured surfaces and in nanoconfined geometries.

Computational evaluation of flight performance of flapping-wing nano air vehicles using hierarchical coupling of nonlinear dynamic and fluid-structure interaction analyses

This study endeavours to introduce an inventive hierarchical coupling methodology for evaluating the flight capabilities of polymer micromachined flapping-wing nano air vehicles (FWNAVs) using advanced computational tools. Furthermore, our objective is to provide a practical demonstration of FWNAVs in both tethered and airborne scenarios. These dual objectives represent the distinctive and pioneering aspects of this research. Such FWNAVs, which are insect-inspired robots, have a 2.5-dimensional structure. An FWNAV comprises a micro piezoelectric drive system and a pair of micro thin flexible wings. The drive system includes a micro transmission and a piezoelectric bimorph actuator. The flight performance of the designed FWNAV is evaluated using a hierarchical coupling approach, where the whole system is decomposed into a micro wing and a micro piezoelectric drive system. The coupling between these parts is modelled as one-way coupling and that between the micro wings and the surrounding air is modelled as strong coupling. In the one-way coupling analysis, nonlinear structural dynamic analysis is conducted for the micro piezoelectric drive system, where the dynamic response is transmitted to the micro wings via the Dirichlet boundary condition. In the strong coupling analysis, strongly coupled fluid-structure interaction analysis is conducted for the micro wings and the surrounding air to consider their strong coupling. The optimisation of flight performance is conducted using fluid-structure interaction analysis to achieve sufficient lift force to support the weight of an FWNAV. The design of a tethered and flyable FWNAV with a size of 10 mm or smaller is demonstrated. This FWNAV can be easily fabricated using polymer micromachining.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial—88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

A Mini Review on Cancer Epigenetics

Cancer epigenetics is the study of epigenetic changes to cancer cells' DNA that don't involve a change in the nucleotide sequence but instead affect how the genetic code is expressed. The complicated disease of cancer is brought on by genetic and epigenetic changes in the regulation of cell division. Our knowledge of the molecular etiology of cancer has substantially advanced and also the discoveries in the fields of cancer genomics and epigenomics, which have improved our comprehension of the development and evolution of tumorigenic processes. The interaction between genetic and epigenetic mutations and their interaction with environmental factors, including our microbiome, that influences cellular metabolism and proliferation rates, must therefore be taken into account in any modern perspective on cancer research. Future genetic and epigenetic therapeutics as well as diagnostics and prognosis will all benefit from the integration and increased understanding of these processes. Here, we tried to give a general summary of the disrupted epigenetic processes in cancer and how they affect the beginning and development of the illness. In conclusion, we talked about how advanced experimental methods and computational tools, such as fresh methods for utilizing enormous data sets, could help us better understand and cure cancer.

Investigations on shear band formation in metallic nanolayered composites

While metallic nanolayered composites exhibit ultrahigh strength, they can fail due to shear bands propagation. Shear bands are affected by many factors, such as layer thickness and stacking fault energy. There is a growing demand to simultaneously prevent shear bands while harnessing the high strength potential derived from the dense interface nanostructures. The mechanisms of shear band formation vary among different nanolayered composites, and some of these mechanisms are investigated. The importance of utilizing advanced computational tools to understand shear band formation is highlighted. This review comprehensively addresses the influencing factors of shear band formation, strategies for shear band suppression, and the underlying mechanisms of shear band formation within metallic nanolayered composites.

UCSF ChimeraX: Tools for structure building and analysis.

Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.

Modeling and optimization of oil adsorption capacity on functionalized magnetic nanoparticles using machine learning approach

Using magnetic nanoparticles (MNPs) has emerged as a promising solution to capture oil from emulsified oily wastewater due to their high oil adsorption capacity, low toxicity, and reusability. Various factors affect the oil adsorption process using MNPs; thus, optimization of the process is required to achieve higher oil adsorption capacity. Smart models based on artificial intelligence (AI) are becoming popular as advanced computational tools to assess the non-linear relationships of variables in complex processes. In this study, least squares support vector machines (LSSVM) hybridized with the coupled simulated annealing (CSA) algorithm, adaptive network-based fuzzy inference system (ANFIS), and optimization techniques such as gene expression programming (GEP) are used to predict the oil adsorption capacity as a target variable. Oil concentration, mixing time, and MNP dosage as effective parameters are selected as input variables. After conducting experiments, 149 data points are obtained, 80 % of which is used in the training process and the remaining 20 % for the testing step. The performances of the developed models are evaluated using statistical parameters, including the coefficient of determination (R2), mean percentage error (MPE), and mean absolute percentage error (MAPE). According to the results, ANFIS and LSSVM-CSA models have a better performance than the GEP model in estimating the oil adsorption capacity with higher values of R2 (>0.99) and smaller relative errors (close to zero) for all training, testing, and total datasets. Detailed model evaluation and error analysis indicate that the LSSVM-CSA model predicts slightly better than ANFIS with the highest R2 of 0.9921 and a very small MAPE = 3.7597 % over the total dataset. Although the developed GEP model shows an acceptable prediction with R2 > 0.95, the higher distribution of relative errors of the developed model results in a larger MAPE. Moreover, the GEP model computational time is considerably greater than that of the other models. The relative importance analysis using Pearson’s and Spearman’s correlation coefficients indicates that the oil concentration and MNP dosage are the most influential variables that affect the oil adsorption capacity.

Nuclear attenuation ability of ternary alumina-borate glass system for medical shielding applications

The present study aims to delve into sodium alumina-borate glass systems doped with transition metals by means of their optical properties and gamma attenuation performance, with a specific focus on their nuclear shielding applications. By leveraging advanced computational tools and simulation techniques, such as FLUKA and XCOM software, this research endeavours to investigate the optical properties and the gamma/radiation shielding ability of the glass system involved. The study seeks to elucidate the fundamental mechanisms governing optical transmission, radiation attenuation, and the overall shielding efficacy of these innovative glass systems. The results show that the optical properties as well as nuclear shielding ability of the glass system, including optical transmission, absorption coefficient, and half value layer (HVL), are significantly affected by Fe2O3 concentrations. Therefore, the incorporation of transition metals leads to enhance the attenuation of gamma rays, indicating an increase in the material's shielding efficiency against incident radiation. The findings of this study have important implications for the design and development of glass materials for various applications, such as nuclear energy, radiation detection, and optical communication.

Enhancing gas production through advanced detailed design engineering: a global perspective on industry practices

Enhancing gas production through advanced detailed design engineering has become a pivotal approach in the global energy sector. This paper explores the integration of cutting-edge engineering methodologies to optimize gas production processes, reduce operational inefficiencies, and meet increasing energy demands. The application of detailed design engineering in gas production involves sophisticated modeling, simulation, and analysis of production systems, ensuring enhanced precision, safety, and scalability. By leveraging advanced computational tools, engineers can predict and mitigate potential production bottlenecks, optimize well designs, and implement efficient gas processing techniques. The global perspective on industry practices highlights regional variations in design standards, regulatory frameworks, and technological adoption. For example, North American gas fields utilize integrated digital twins and automation technologies to streamline production, while regions like the Middle East emphasize design optimization in offshore gas extraction. Additionally, the development of sustainable practices, such as carbon capture and storage (CCS) and energy-efficient processing units, is becoming a critical focus for gas producers striving to reduce environmental impact and align with international climate goals. This study also examines the role of collaborative frameworks between engineering teams and production operators in fostering innovation and knowledge transfer, further driving the refinement of detailed design approaches. The future of gas production will depend significantly on advancements in materials science, data analytics, and artificial intelligence, which are expected to revolutionize the design and operation of gas production systems. In conclusion, enhancing gas production through advanced detailed design engineering offers the potential to unlock new efficiencies, improve safety standards, and contribute to global energy security. However, the successful implementation of these practices requires continuous investment in research, technology, and cross-industry collaboration.

Do you see what I see? Measuring the semantic differences in image‐recognition services' outputs

AbstractAs scholars increasingly undertake large‐scale analysis of visual materials, advanced computational tools show promise for informing that process. One technique in the toolbox is image recognition, made readily accessible via Google Vision AI, Microsoft Azure Computer Vision, and Amazon's Rekognition service. However, concerns about such issues as bias factors and low reliability have led to warnings against research employing it. A systematic study of cross‐service label agreement concretized such issues: using eight datasets, spanning professionally produced and user‐generated images, the work showed that image‐recognition services disagree on the most suitable labels for images. Beyond supporting caveats expressed in prior literature, the report articulates two mitigation strategies, both involving the use of multiple image‐recognition services: Highly explorative research could include all the labels, accepting noisier but less restrictive analysis output. Alternatively, scholars may employ word‐embedding‐based approaches to identify concepts that are similar enough for their purposes, then focus on those labels filtered in.

Optimized fire design for steel industrial sheds

AbstractThis paper showcases some of the main contributions from the research project Switch2Steel, in which a web application is developed for the calculation and optimization of the cost of steel industrial sheds. Unprotected steel structures, compared to other construction materials, possess relatively low fire resistance. To enhance their fire performance, adequate fire protection is required. Simplified fire design, without considering the real influence of the main parameters on the development and propagation of natural fires, often leads to excessive amounts of costly protective material. On the other hand, the use of advanced software tools may be impractical because of iterative time‐consuming computational processes such as structural optimization. Hence, this work presents a simple user‐friendly calculation tool that allows users to define the characteristics of the sheds to be built and based on a set of metrics, weights, and advanced algorithms, to minimize their cost by reducing the consumption of steel and fire protection material. To illustrate the use of the tool, a reference case of a steel industrial shed is selected and the results obtained by the tool are compared with those obtained by FEM tools. The tool gives more optimized results than the use of prescriptive rules, and the results are obtained considerably faster than by advanced expensive computational tools.

Delineating Management Zones with Different Yield Potentials in Soybean–Corn and Soybean–Cotton Production Systems

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations.

Optimizing Sulfur Utilization in High Loading Cathode for All-Solid-State Lithium-Sulfur Battery

All Solid-state Lithium-Sulfur battery (ASSLSB) possesses a great promise in providing a safe, high energy density, and low cost battery technology for vehicle electrification and grid energy storage. To achieve practically high specific energy in ASSLSB (e.g., >400 Wh/Kg), sulfur cathodes with high areal capacity (>6 mAh cm-2) are essentially needed to offset the relatively low working voltage of elemental sulfur cathode (1.9 V vs. Li). This requires high mass loading sulfur cathodes by maximizing both sulfur content in the whole electrode and sulfur utilization rate to reduce the parasitic weights. An optimal electrode architecture featuring sufficiently connected sulfur/solid electrolyte/carbon triple interfaces is desired to enable sulfur’s fast accessibility to electron and Li-ion simultaneously. Due to the non-flowability of those three solid components, however, it is very hard to maximize the contact of trip solid phases, which need new insights of the materials design and processing technology.This work looks at carbon microstructures and their impact on triple-point contact between electrochemically active sulfur, carbon, and the solid-state electrolyte (SSE) that comprises most composite cathodes in ASSLSB systems. While significant work has gone into understanding and developing novel carbon host structures for liquid Li-S cells for improving sulfur utilization, polysulfide trapping, better understanding is needed for ASSLSBs. Specifically, how sulfur infiltration, loading, and spatial distribution within the porous carbon host impacts sulfur utilization. Understanding how the infiltrated sulfur that resides in the porous carbon host connects with both carbon and solid electrolyte networks across the whole electrode structure and maintains its robustness during the repeated cell cycling is needed to achieve full utilization in high sulfur loading electrodes. The solid sulfur cathode system examined in the present study is a composite of porous carbon, sulfur, and a sulfide-based solid electrolyte. A combination of porous carbon hosts is investigated and correlated to the cell performance at practical conditions. Advanced characterization and computational tools are used to further elucidate the key parameters for high sulfur utilization in ASSLSBs with high sulfur loading cathodes. Details of the study will be presented and discussed at the meeting.Reference:(1) Nagata, H. and Y. Chikusa (2016). "All-Solid-State Lithium–Sulfur Battery with High Energy and Power Densities at the Cell Level." Energy Technology 4(4): 484-489(2) Banerjee, A., et al. (2020). "Interfaces and Interphases in All-Solid-State Batteries with Inorganic Solid Electrolytes." Chemical Reviews 120(14): 6878-6933.

Exploring α, β-unsaturated carbonyl compounds against bacterial efflux pumps via computational approach

Antibiotic resistance has become a pressing global health crisis, with bacterial infections increasingly difficult to treat due to the emergence of multidrug resistance. This study aims to identify potential chalcone molecules that interact with two key multidrug efflux pumps, AcrB and EmrD, of Escherichia coli, using advanced computational tools. In silico ADMET (absorption, distribution, metabolism, excretion, and toxicity), drug-likeness prediction, molecular docking, and molecular dynamics simulation analyses were conducted on a ligand library comprising 100 chalcone compounds against AcrB (PDB: 4DX5) and EmrD (PDB: 2GFP). The results demonstrated that Elastichalcone A (PubChem CID 102103730) exhibited a remarkable binding affinity of −9.9 kcal/mol against AcrB, while 4'-methoxy-4-hydroxychalcone (PubChem CID 5927890) displayed a binding affinity of −9.8 kcal/mol against EmrD. Both ligands satisfied drug-likeness rules and possessed favorable pharmacokinetic profiles. Molecular dynamics simulation of the AcrB-Elastichalcone A complex remained stable over 100 ns, with minimal fluctuations in root-mean-square deviation and root-mean-square fluctuation. The screened ligand library demonstrated good drug-likeness and pharmacokinetic properties. Moreover, the MM/PB(GB)SA calculation indicated the tight binding and thermodynamic stability of the simulated protein-ligand complexes. Overall, this study highlights the potential of chalcones as promising candidates for targeting multidrug efflux pumps, offering a potential strategy to overcome antibiotic resistance. Further exploration and optimization of these compounds may lead to the development of effective therapeutics against multidrug-resistant bacterial infections. Communicated by Ramaswamy H. Sarma

Protein engineering of pores for separation, sensing, and sequencing.

Proteins are critical to cellular function and survival. They are complex molecules with precise structuresand chemistries, which allow them to serve diverse functions for maintaining overall cell homeostasis. Since the discovery of the first enzyme in 1833, a gamut of advanced experimental and computational tools has been developed and deployed for understanding protein structure and function. Recent studies have demonstrated the ability to redesign/alter natural proteins for applications in industrial processes of interest and to make customized, novel synthetic proteins in the laboratory through protein engineering. We comprehensively review the successes in engineering pore-forming proteins and correlate the amino acid-level biochemistry of different pore modification strategies to the intended applications limited to nucleotide/peptide sequencing, single-molecule sensing, and precise molecular separations.

Tailoring the Inherent Properties of Biobased Nanoparticles for Nanomedicine.

Biobased nanoparticles are at the leading edge of the rapidly developing field of nanomedicine and biotherapeutics. Their unique size, shape, and biophysical properties make them attractive tools for biomedical research, including vaccination, targeted drug delivery, and immune therapy. These nanoparticles are engineered to present native cell receptors and proteins on their surfaces, providing a biomimicking camouflage for therapeutic cargo to evade rapid degradation, immune rejection, inflammation, and clearance. Despite showing promising clinical relevance, commercial implementation of these biobased nanoparticles is yet to be fully realized. In this perspective, we discuss advanced biobased nanoparticle designs used in medical applications, such as cell membrane nanoparticles, exosomes, and synthetic lipid-derived nanoparticles, and highlight their benefits and potential challenges. Moreover, we critically assess the future of preparing such particles using artificial intelligence and machine learning. These advanced computational tools will be able to predict the functional composition and behavior of the proteins and cell receptors present on the nanoparticle surfaces. With more advancement in designing new biobased nanoparticles, this field of research could play a key role in dictating the future rational design of drug transporters, thereby ultimately improving overall therapeutic outcomes.

Hybrid discrete-continuum multiscale model of tissue growth and remodeling.

Tissue growth and remodeling (G&R) is often central to disease etiology and progression, so understanding G&R is essential for understanding disease and developing effective therapies. While the state-of-the-art in this regard is animal and cellular models, recent advances in computational tools offer another avenue to investigate G&R. A major challenge for computational models is bridging from the cellular scale (at which changes are actually occurring) to the macroscopic, geometric-scale (at which physiological consequences arise). Thus, many computational models simplify one scale or another in the name of computational tractability. In this work, we develop a discrete-continuum modeling scheme for analyzing G&R, in which we apply changes directly to the discrete cell and extracellular matrix (ECM) architecture and pass those changes up to a finite-element macroscale geometry. We demonstrate the use of the model in three case-study scenarios: the media of a thick-walled artery, and the media and adventitia of a thick-walled artery, and chronic dissection of an arterial wall. We analyze each case in terms of the new and insightful data that can be gathered from this technique, and we compare our results from this model to several others. STATEMENT OF SIGNIFICANCE: This work is significant in that it provides a framework for combining discrete, microstructural- and cellular-scale models to the growth and remodeling of large tissue structures (such as the aorta). It is a significant advance in that it couples the microscopic remodeling with an existing macroscopic finite element model, making it relatively easy to use for a wide range of conceptual models. It has the potential to improve understanding of many growth and remodeling processes, such as organ formation during development and aneurysm formation, growth, and rupture.

Real-world study to evaluate the clinical performance of a computational method in precision oncology.

e13631 Background: In recent years, precision medicine has increasingly been integrated into routine clinical oncology care. However, interpretation of large amounts of biological information can be challenging in daily clinical practice settings. We previously demonstrated that digital drug assignment (DDA), an artificial intelligence-based computational method that ranks associated targeted therapies based on the totality of available genomic data rather than matching one drug to one biomarker, was predictive of relative benefit of the agents as used in the SHIVA01 trial (Petak et al., 2021, doi: 10.1038/s41698-021-00191-2). Here, we collected real-world clinical outcome data from patients with solid tumors who received decision support where DDA was integrated to aid a molecular tumor board (MTB) and investigated the effectiveness of recommended therapies. Methods: Between 2016 and 2021, 208 patients with solid tumors were involved in our precision oncology program (69% gastrointestinal, 14% gynecological, 9% breast, 8% other tumors). In most cases targeted panel sequencing was carried out (50-gene panel: 51%, 591-gene panel: 45%). Classification of the detected alterations and therapeutic ranking was performed by the DDA software tool as previously described. The output was assessed by the MTB that provided a strategy to the clinicians who made the final therapy decisions. DDA output scores were supportive for all molecularly targeted agents (MTAs) administered. Treatment lines after decision support with MTAs were compared with lines of standard agents (STs) retrospectively and evaluated by best overall response, log-rank test of progression-free survival (PFS), and durable clinical benefit (DCB). Results: Of all 208 patients, 81 were treated with MTAs and 59 with STs after DDA, implying 114 and 97 therapeutic lines, respectively. Disease control rate (DCR) of the suggested MTAs was 40% (0 CR, 9 PR, 37 SD, 43 PD, 25 lost to follow-up), while DCR for STs was 25% (1 CR, 3 PR, 20 SD, 48 PD, 25 lost) ( p=0.025). PFS value was available for 88 MTA and 71 ST lines. Log-rank test revealed a significantly longer median PFS for MTAs than STs: 4 vs. 2.5 months, respectively ( p&lt;0.001). Proportion of patients who reached DCB of 6 months was 32% in the MTA and 11% in the ST group ( p&lt;0.001). DCB at 9 months was 19% and 5% in the MTA and ST cohorts, respectively ( p=0.003). Conclusions: This study revealed clear clinical benefit for targeted therapies over conventional treatments used in daily practice, where MTB was aided by a computational tool to interpret complex molecular data. After the previous clinical validation, here we show that integration of DDA into real-world clinical setting is feasible and safe. The results fit well into the trend of targeted therapies becoming a routine procedure and underscore the importance of precision oncology decision support by advanced computational tools.

Data-driven ultimate condition prediction of UHPC columns subjected lateral confinement using Artificial Neural Network (ANN) approach

Despite the superior mechanical properties of Ultra-High Performance Concrete (UHPC) in comparison to conventional concrete, UHPC is still not widely used in the construction industry due to the lack of design standards. This research mainly focuses on using advanced computational tools for the prediction of ultimate stress and its corresponding strain for confined UHPC columns, to facilitate the process of analysis and design, and to support further development of standards. Characteristic parameters of confined UHPC column samples were extracted from available literature as the input, and the corresponding ultimate stress and strain as the output. Artificial Neural Network (ANN) technique was adopted to process the input parameters in order to train and predict the output. In the regression analysis, the trained networks exhibited the coefficients of determination (R2) of 0.995 and 0.964 for the prediction of ultimate stress and corresponding strain respectively. The weights and biases within the best performance ANN from the k-fold cross validation can be extracted to perform mathematical solutions for the prediction in design.