Computational Analysis Methods Research Articles

Computational homogenization method for the bending analysis of submarine power cables

Computational homogenization method for the bending analysis of submarine power cables

Trigger mechanism centralized but discursive frame fragmented: China as a case for studying populism at the grassroots level in the social media age

ABSTRACT While most research focuses on top-down populism practiced by political actors in democratic countries, we paid attention to populist phenomena at the grassroots level in an authoritarian context. Specifically, we proposed a perspective of investigating populist discourse embedded in public opinion and adopted a fine-grained discursive frame approach to study Chinese online populism. Using large-scale social media data around trending events and employing cutting-edge computational semantic analysis methods, we discovered that economic anxiety is central to the trigger mechanism of Chinese online populism, and the specific populist discourse is fragmented in terms of ideological resources, cultural toolboxes, and semantic structure. This study not only provides a more comprehensive understanding of contemporary Chinese populism phenomena, but it also contributes to global populism research by providing valuable empirical evidence in authoritarian contexts and broadening the theoretical perspective of studying populism at the grassroots level.

CFD and Wind Tunnel Study on Cricket Ball at Different Seam Angle and Velocity

This study delves into the intriguing investigation of flow patterns over a cricket ball, employing both computational and experimental methods for a comparative analysis. The work explores about variation of velocities in different angles, here we have used as 4 cricket balls they are dukes cricket balls, SG cricket balls, dukes 3d printed cricket ball and SG printed cricket ball. While comparing all the cricket balls it shows values in the wind tunnel experiment in seam angles of 0°,30°,45°,60°& 90° in a subsonic wind tunnel. Both computational simulations and experimental observations were conducted to analyze the impact of seam angles and velocities on the cricket balls. The results obtained from computational models were successfully validated through experimental data, affirming the reliability of the computational approach in predicting aerodynamic characteristics. Flow visualization techniques further confirm these findings, highlighting the significance of keywords such as drag, seam angles, reverse swing, flow visualization, and computational fluid dynamics (CFD) in understanding the aerodynamics of cricket balls. Overall, this research provides valuable insights into optimizing bowling strategies by considering the interaction between seam angles, velocities, and aerodynamic behavior of cricket balls. Keyword: Ball drag analysis, Reverse swing analysis, CFD analysis, Cricket ball analysis.

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

Fiber-reinforced friction materials: Experimental, statistical and computational universal analysis

In this paper, the experimental, statistical and computational universal analysis methods applicable to most fiber-reinforced friction materials (FMs) are presented for four natural wollastonite fibers (WFs) with different length-to-diameter (L/D) ratios. Machine learning (ML) methods for friction coefficient prediction of friction materials are compared and screened. Numerical statistics and calculations are used in conjunction with the use of finite element (FE) models to determine the performance metrics and friction wear mechanisms of FMs. The experimental and computational results show that the artificial neural network (ANN) model performs the best in COF prediction with an accuracy of 0.961. The friction fluctuation, friction stability and integrated friction performance of the WF-containing samples are better than those of the blank samples. For the first time, the FE model was used to explain the swirling phenomenon on the wear surface of FMs. This study provides new ways and ideas for the design, experimentation, data processing and application of friction composites.

Assessment of Age-related Histopathological Changes Occurring in the Lacrimal Gland of Aging Wistar Rats

Purpose. Wistar rats are often used in ophthalmological studies and in testing substances having therapeutic effects. The condition of the eyes of these animals, as well as other mammals, including humans, in the normal state, pathology, and aging, depends on the work of the lacrimal glands. The aim of this study was to develop convenient approaches for determining age-dependent histopathological changes in the exorbital lacrimal glands (ELGs) of aging rats.Material and methods. Isolated ELGs obtained from Wistar rats aged of 8 and 11 months served as a material of the study. Changes in the structure of ELGs were evaluated using methods of computer analysis of morphological patterns and quantitative data processing.Results. The functioning and state of the ELGs are reflected in their dramatic structural changes and progressive inflammation. Our study proposes a simple way to assess the state of ELGs in aging rats using methods of morphology and computer programs. Using the proposed approach, it was shown that in the period from 8 to 11 months of postnatal development, significant age-related changes occur in aging rats. Changes manifest in the progress of parenchymal degeneration and progressive infiltration of the tissue by inflammatory cells.Conclusions. The proposed method of ELG state’s evaluation can be used in ophthalmological studies, and in particular, when studying the development of “dry eye” syndrome and testing the means of its therapy.

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

Accurate identification of protein nucleic acid binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large scale protein language model. Specifically, our multi modal approach leverages a contrastive learning technique and atom wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state of the art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1 Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.

Abstract 2018: Tumor treating fields (TTFields) disrupt cancer cell invasion by impacting cell-ECM traction forces

Abstract Introduction: Tumor Treating Fields (TTFields) are an anti-cancer treatment that use low intensity, alternating electric fields. Anti-mitotic treatment effects of TTFields have been, to date, considered the primary anti-cancer effect. Recent studies have shown additional impacts on cancer cell invasion and migration, among other effects, resulting in a complex anti-tumor response derived from a combination of factors. By deploying computational imaging tools to assess experimental conditions that apply TTFields treatment to 3D cancer multicellular spheroid cultures, the goal of our study is to elucidate important mechanisms and effect sizes on anti-tumor effects relating to the modulation of cancer cell-extracellular matrix (ECM) biophysical interactions. Methods: 3D multicellular tumor spheroids (MTS) are cultured using GFP-tagged U-87 MG glioma cells. A single 3D MTS is placed in a cell culture insert and embedded in collagen I ECM at 2.25 mg/mL. Fluorescent polystyrene microspheres are dispersed in the ECM to facilitate subsequent tracking of displacement fields induced by the MTS-ECM construct. Fluorescence microscopy imaging is acquired for all spheroids prior to treatment initiation. Then, with 3 replicates in each treatment condition, TTFields are applied for a duration of 48 hours at 200 kHz frequency with a no treatment condition as a control. At 48 hours, we acquire fluorescence microscopy images to track spheroid growth and bead-field displacement. To determine deformation fields induced by the cancer spheroid, we compare each image at 48 hours to their respective baseline image. Deformation fields are analyzed using a custom-developed analysis code that performs an initial alignment of the baseline and specific time point images using a global rigid registration. Next, we perform a B-spline based nonrigid image registration to generate an estimate of the deformation field vector at each pixel location within the image. Deformation intensity is measured within a circular region of interest (r = 900 µm) centered at the spheroid core. Results: The average magnitude of displacement for treatment and control groups is 0.52±0.2 µm and 1.07±0.1 µm, respectively. Analysis of deformation fields reveals a significant difference (p < 0.1) in the magnitude of deformation present between treatment and control groups. Conclusions: Implementation of our computational imaging analysis methods using rigid and nonrigid registration, we observe a 69.6% decrease in the magnitude of deformation in TTFields treated spheroids compared to control spheroids. Due to the decrease in deformation induced by cancer spheroids treated with TTFields, we conclude that TTFields impact cancer cell traction forces applied to the ECM to disrupt cancer cell invasion. Citation Format: Samantha M. Short, Haley J. Bowers, Jared A. Weis. Tumor treating fields (TTFields) disrupt cancer cell invasion by impacting cell-ECM traction forces [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2018.

A LAPACK Implementation of the Dynamic Mode Decomposition

The Dynamic Mode Decomposition (DMD) is a method for computational analysis of nonlinear dynamical systems in data driven scenarios. Based on high fidelity numerical simulations or experimental data, the DMD can be used to reveal the latent structures in the dynamics or as a forecasting or a model order reduction tool. The theoretical underpinning of the DMD is the Koopman operator on a Hilbert space of observables of the dynamics under study. This paper describes a numerically robust and versatile variant of the DMD and its implementation using the state-of-the-art dense numerical linear algebra software package LAPACK . The features of the proposed software solution include residual bounds for the computed eigenpairs of the DMD matrix, eigenvectors refinements and computation of the eigenvectors of the Exact DMD, compressed DMD for efficient analysis of high dimensional problems that can be easily adapted for fast updates in a streaming DMD. Numerical analysis is the bedrock of numerical robustness and reliability of the software, that is tested following the highest standards and practices of LAPACK . Important numerical topics are discussed in detail and illustrated using numerous numerical examples.

Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public Participation

Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpuses and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modeling.

ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

BackgroundInteraction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research.ResultsTo address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis.ConclusionsORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events.

Daylight Diagram – A Method to Map and Analyse the Temporal Conditions of Daylight Intensity

This study proposes and examines a new method and diagram for revealing and analysing temporal and transitional daylight conditions based on computational modelling and simulations. For a human to experience and decipher the world, daylight is a predominant resource to engage with the surroundings. With humans spending the prevalent time inside buildings, it is important to understand and design the daylight environments in the early phases of design processes. The aim is to retain and reveal the information of high-resolution simulations and to explore, test and verify the capacities of the method in comparison to Illuminance, Daylight Factor and Daylight Autonomy values. The research design uses digital modelling and representation techniques combined with computational simulation methods for daylight analysis, evaluation and communication. Eight digital test cases show that the proposed Daylight Diagram enables the observation of temporal daylight dynamics. The proposed method and diagram employ existing simulation systems, allowing a fast integration and use in early-phase design processes as a key instrument to design advanced daylight phenomena and conditions focusing on transitional and temporal daylight intensities.

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV—post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

Multiscale analysis of recycled coarse aggregate concrete under the synergistic action of KH560 and PVA fibers

Considering the long-term working performance of the waste concrete building elements, this study used 3-Glycidoxypropyltrimethoxysilane (KH560) and Polyvinyl alcohol (PVA) fibers to modify the recycled concrete (RC) with the aim of improving the mechanical and long-term properties of RC. In this study, the mechanical properties of recycled coarse aggregate (RCA) concrete before and after modification were systematically investigated. The macroscale results showed that KH560 compensates for the reduced compressive strength of RCA concrete due to PVA fibers. The synergistic effect of PVA and KH560 substantially increased the flexural and shear strength of RCA concrete, with a maximum increase of 22.6% in flexural strength when the RCA substitution rate was 80%. The results of three microscopic tests (SEM / FTIR/XRD) revealed that KH560 increased the content of cement gel, which then optimized the internal pore structure of RCA concrete. The chemical reaction between KH560 and cement produced SiKH560-OKH560-SiCSH. At the nano-scale, computational analysis methods in atomistic modeling techniques were employed to model PVA fibers, cement and KH560, and the dynamic and static simulation results of the model were combined to discover a consistent SiKH560-OKH560-SiCSH chemical bond between KH560/CSH and both old and new CSH, Additionally, the findings revealed the coexistence hydrogen and ionic bonds. KH560 improves the interaction between PVA fibers and cement by forming numerous hydrogen bonds, and resolves the weak interface between the new and old cement through stable chemical bonds.

DbCRAF: a curated knowledgebase for regulation of radiation response in human cancer.

Radiation therapy (RT) is one of the primary treatment modalities of cancer, with 40-60% of cancer patients benefiting from RT during their treatment course. The intrinsic radiosensitivity or acquired radioresistance of tumor cells would affect the response to RT and clinical outcomes in patients. Thus, mining the regulatory mechanisms in tumor radiosensitivity or radioresistance that have been verified by biological experiments and computational analysis methods will enhance the overall understanding of RT. Here, we describe a comprehensive database dbCRAF (http://dbCRAF.xialab.info/) to document and annotate the factors (1,677 genes, 49 proteins and 612 radiosensitizers) linked with radiation response, including radiosensitivity, radioresistance in cancer cells and prognosis in cancer patients receiving RT. On the one hand, dbCRAF enables researchers to directly access knowledge for regulation of radiation response in human cancer buried in the vast literature. On the other hand, dbCRAF provides four flexible modules to analyze and visualize the functional relationship between these factors and clinical outcome, KEGG pathway and target genes. In conclusion, dbCRAF serves as a valuable resource for elucidating the regulatory mechanisms of radiation response in human cancers as well as for the improvement of RT options.

Edge roughness analysis in nanoscale for single-molecule localization microscopy images

Abstract The recent advances in super-resolution fluorescence microscopy, including single-molecule localization microscopy (SMLM), has enabled the study of previously inaccessible details, such as the organization of proteins within cellular compartments and even nanostructures in nonbiological nanomaterials, such as the polymers and semiconductors. With such developments, the need for the development of various computational nanostructure analysis methods for SMLM images is also increasing; however, this has been limited to protein cluster analysis. In this study, we developed an edge structure analysis method for pointillistic SMLM images based on the line edge roughness and power spectral density analyses. By investigating the effect of point properties in SMLM images, such as the size, density, and localization precision on the roughness measurement, we successfully demonstrated this analysis method for experimental SMLM images of actual samples, including the semiconductor line patterns, cytoskeletal elements, and cell membranes. This systematic investigation of the effect of each localization rendering parameter on edge roughness measurement provides a range for the optimal rendering parameters that preserve the relevant nanoscale structure of interest. These new methods are expected to expand our understanding of the targets by providing valuable insights into edge nanoscale structures that have not been previously obtained quantitatively.

Proposing a criteria set for site selection for new hospital construction in Ho Chi Minh City

The development of an evaluation criteria set for optimal new hospital site selection is a complex task, requiring the consideration of numerous socioeconomic, geographical, and environmental factors. This study proposes a set of 16 criteria based on an extensive analysis of previous studies and inputs from multidisciplinary experts. The criteria reflect the economic, social, and infrastructural challenges characteristic of Ho Chi Minh City, including population density, accessibility, traffic conditions, and flooding. Notably, the criteria set emphasizes the importance of healthcare service provision, human resources, and environmental and social impacts when constructing a new hospital. The research outcome is a hospital site selection criteria set tailored for Ho Chi Minh City, laying the foundation for experts and scientists to apply computational and multi-criteria analysis methods to determine suitable locations. The criteria can also be adapted for areas similar to Ho Chi Minh City.

In Silico Models to Validate Novel Blood-Based Biomarkers.

Biological validation of preliminary findings is a key prerequisite in biomarker discovery. In recent years, the development of advanced large-scale sequencing technologies combined with high-throughput computational analysis methods led to the extraction of considerable amount of data from healthy and diseased tissues. Stored in large open-access repositories, these data can be accessed and interrogated by researchers aiming at understanding the biological rationale behind their results. These so called in silico analyses, in opposite to in vitro analyses, have gained increasing importance in recent years, becoming a major component of research projects and publications. However, making sense of the large amount of data available can be challenging and may lead to a misinterpretation of the data. To reduce the dimensionality of this data, recent years have seen the development of statistical m\ethods and advanced graph analytics which help researchers summarize the available data and draw appropriate conclusions. In this chapter we will describe three in silico methods to investigate the biological underpinnings of a panel of seven blood-based biomarkers of Alzheimer's disease.

Thymol and carvacrol derivatives as anticancer agents; synthesis, in vitro activity, and computational analysis of biological targets.

Various thymol and carvacrol derivatives have been synthesized to test the anticancer activity potential. Computational methods including network pharmacology and molecular docking approaches were utilized to identify and assess the potential biological targets relating to cancer. Amongst the synthesized derivatives the ethoxy-cyclohexyl analogues were consistently the most active against a panel of 10 different cancer cell lines covering a variety of origins. Biological target predictions revealed the AKT1 protein to be a core and central target of the most active compounds. Molecular docking identified a binding pocket within this protein in which the most active compounds bind. The incorporation of computational analysis methods and conventional structure-activity approaches identified analogues of thymol and carvacrol with the highest anticancer potential, and analyzed their possible biological targets in a comprehensive manner.

TRACING MEDIA SOLIDARITIES WITH MUSLIMS: CONTESTING ISLAMOPHOBIA ON TWITTER

Solidarity has been cited as a necessary prerequisite for transformational structural change and therefore contains revolutionary potential (Featherstone, 2012). This paper examines the role and evolution of mediated solidarities, which have become increasingly central to an analysis of social movements with the advent of participatory technologies, by drawing on data from a project on anti-Islamophobic counter-narratives. Online platforms have the affordances to contest Islamophobic hate speech as demonstrated by the dynamics of #stopIslam following the Brussels terror attack, 2016. In this instance, the hashtag gained its prominence through the contestations of users who sought to question, critique and undermine its original message (reference redacted). However, research has also shown the limitations of social media for online activism, in particular for creating meaningful debates or change (Schradie, 2019). This paper examines data from a large-scale study that used methods of computational, quantitative, and qualitative analysis to examine the dynamics of discourse about Muslims on Twitter in the case of Brexit, the Christchurch terror attack and Covid. We will examine whether the high incidence of solidarity discourses in this dataset are limited to acts of counter-speech (and other acts of weak solidarity) or if they contribute to sustainable counter-narratives that have implications for wider discursive formations related to Islam. Rather than reinforce existing binary arguments regarding the potentials and limitations of Twitter as a platform for solidarity, we wish to demonstrate the contradictory dynamics of the solidarities that arise from the logics of Twitter which relies on and produces these entanglements.