Computational Analysis Methods Research Articles

Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment”

This study employs computational hermeneutics to explore emotional representations in Fyodor Dostoevsky’s Crime and Punishment. Using advanced Natural Language Processing (NLP) techniques, including sentiment analysis powered by the BERT model and other tools, we examine the emotional and existential aspects embedded in the narrative. Our approach combines computational tools with traditional hermeneutic methods to deconstruct the emotional journey of the protagonist, Rodion Raskolnikov. We quantitatively analyze his emotional evolution before and after the pivotal crime, mapping his psychological transition from detachment to engagement. The study focuses on four key emotional dimensions: Attitude, Introspection, Sensitivity, and Temper. By transforming qualitative textual data into a quantitative framework, we provide a novel illustration of Raskolnikov’s psychological progression from a state of depersonalization to full emotional contact. This computational analysis is contextualized within a broader philosophical interpretation, drawing on concepts such as Stoic apatheia and Heideggerian Sorge. Our research serves a dual purpose: it enhances our understanding of Dostoevsky’s work while demonstrating the potential of computational methods in literary analysis. By bridging digital humanities and traditional literary criticism, this study contributes to the growing field of digital hermeneutics and opens new avenues for interdisciplinary research in literature, philosophy, and computational linguistics.

Shaping the Narrative: Examining News Coverage of Voter ID Laws in the United States

ABSTRACT This study is the first to examine US news coverage of voter ID laws. Voter ID requirements are central to ongoing political debates about voting procedures. Despite rare cases of voter fraud by impersonation and the strong polarization over voter ID among partisan elites, there is a surprising broad bipartisan support for voter ID among the American public. Using a range of inductive computational content analysis methods, we analyse news coverage of voter ID requirements from 2013 to 2023. We find coverage differs by outlets political leaning and demonstrate a link between news media discourse and elite framing on the issue. Donald Trump’s voter fraud allegations, however, appear to have substantially shaped left-leaning media coverage from 2016, raising concerns around whether this might have helped amplify electoral conspiracies, instead of offering the public alternative ways of thinking about voter ID laws.

Impact of Binder Selection on Functional Properties of Polymer Nanocomposite Featured with Metal Oxide Nanoparticle

The effectiveness of nanocomposite materials depends on the accuracy of the binder selection process. This process is crucial for ensuring compatibility, better adhesive behaviour, and stability during the synthesis of polymer composites with metal nanoparticles. An investigation was conducted to analyze the accuracy and efficiency of binder materials on metal nanoparticle (oxide) featured polymer composites. The investigation utilized Advanced Spectroscopic Computational Optimization Analysis (ASCOA) and high-throughput experimental methods to identify and optimize binders using computational modelling (ACM). The processes driving binder-nanoparticle interactions were unravelled by combining advanced characterization methods such as Transition Metal Oxide-based Materials (TMO-M), non-hydrolyzable sialic acid (N-HSIA), and oxygen reduction reaction process (ORR) investigations. In-depth simulation analyses were performed to verify the effectiveness of the proposed methods and gain further understanding. These simulations investigated alternative binding arrangements to maximize material performance while considering sustainability and economic factors. The results demonstrated how binder choice affects material properties, which can be valuable for designing better nanocomposite materials for specific purposes. Additionally, the ASCOA and ACM analyses revealed an accuracy ratio of more than 90% and efficiency ratios of 86% and 84% for ASCOA and ACM, respectively.

Materiality & light: case studies exploring architectural surfaces informed by light behavior

This paper outlines three case studies that explore the relationship between form, material behavior, and light performance. Each case study appropriates methods of computational analysis, parametric design, and digital fabrication to construct directed light fields and patterns from architectural surfaces in public spaces. Each project explores how controlled conditions of materiality and light phenomena may provide opportunities to reengage the physicality of human experience by generating effects that attempt to enhance human interaction and spatial perception.

Abstract C049: Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC

Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most challenging cancers to treat due to its aggressive nature and the difficulties in early detection. The disease progresses rapidly from stage T1 to T4, leaving a narrow window for effective intervention. This swift progression is closely tied to significant metabolic alterations, and current studies have highlighted specific metabolic changes. This includes elevated glutamine metabolism and altered aspartate and alpha-ketoglutarate production to support uncontrolled tumor growth; and elevated lysophosphatidylcholines secretion by cancer-associated fibroblasts to support tumor progression. These insights underscore the need for a holistic understanding of PDAC metabolism. Hence, there is an urgent need for a comprehensive and high-resolution analysis of PDAC's metabolic landscape remains unexplored. To computationally characterize the PDAC metabolism and fully leverage the large-scale transcriptomics with matched or unmatched metabolomics data, we developed an algorithm based on the Michaelis-Menten equation to estimate distribution-wise variations in mass-carrying metabolic networks using gene expression and metabolomics data. We start by manually curating a PDAC-specific metabolic network that focuses on central metabolism, glutamine-glutamate metabolism, and lipid metabolism. We then gathered comprehensive kinetic parameters, predicted enzyme activity from transcriptomics data, and conducted metabolomic profiling to determine metabolic compound concentrations. By applying Monte Carlo sampling methods, we achieved an overall balance of metabolic flux, enabling us to model reaction rates accurately and predict metabolic shifts. This approach holds potential to provide a comprehensive perspective on the rewired metabolism in PDAC, inspiring the development of metabolic targets or diet interventions for therapeutic treatment. Citation Format: Sha Cao, Haiqi Zhu, Yijie Wang, Melissa Fishel, Chi Zhang. Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C049.

Computational Methods for Image Analysis in Craniofacial Development and Disease.

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

A variational-based non-smooth contact dynamics approach for the seismic analysis of historical masonry structures

A variational formulation of the non-smooth contact dynamics method is proposed to address the dynamic response of historical masonry structures modeled as systems of 3D rigid blocks and subjected to ground excitation. Upon assuming a unilateral-frictional contact law between the blocks, the equations of motions are formulated in a time-discrete impulse theorem format in the unknown block velocities and contact impulses. The variational structure of the problem to be solved at each time step is proven. On that basis, the numerical method requires at each time step to perform a collision detection that identifies antagonist contact points based on the given structural configuration, to solve a second-order conic programming problem that outputs block velocities and contact impulses, and to update the structural configuration for the solution to advance in time. As a merit of the formulation, large-scale problems can be robustly and efficiently addressed thanks to the convex setting of the time-step optimization problem. Numerical results are presented to test the computational performances of the proposed approach. Benchmark problems provide numerical evidence that the formulation is consistent with event-driven solutions based on the classical Housner impact model. The dynamic response, failure domains, and fragility functions of real-size masonry structures are then explored under ground impulse or earthquake excitation. The obtained results prove the reliability of the present computational method for the dynamic analysis and seismic assessment of historical masonry constructions of engineering interest.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model.

Immune cells undergo cytokine-driven polarization in respond to diverse stimuli. This process significantly modulates their transcriptional profiles and functional states. Although single-cell RNA sequencing (scRNA-seq) has advanced our understanding of immune responses across various diseases or conditions, currently there lacks a method to systematically examine cytokine effects and immune cell polarization. To address this gap, we developed Single-cell unified polarization assessment (Scupa), the first computational method for comprehensive immune cell polarization analysis. Scupa is trained on data from the Immune Dictionary, which characterizes 66 cytokine-driven polarization states across 14 immune cell types. By leveraging the cell embeddings from the Universal Cell Embeddings model, Scupa effectively identifies polarized cells in new datasets generated from different species and experimental conditions. Applications of Scupa in independent datasets demonstrated its accuracy in classifying polarized cells and further revealed distinct polarization profiles in tumor-infiltrating myeloid cells across cancers. Scupa complements conventional single-cell data analysis by providing new insights into immune cell polarization, and it holds promise for assessing molecular effects or identifying therapeutic targets in cytokine-based therapies.

Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology

Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology

On Measurement Validity and Language Models: Increasing Validity and Decreasing Bias with Instructions

ABSTRACT Language models like BERT or GPT are becoming increasingly popular measurement tools, but are the measurements they produce valid? Literature suggests that there is still a relevant gap between the ambitions of computational text analysis methods and the validity of their outputs. One prominent threat to validity is hidden biases in the training data, where models learn group-specific language patterns instead of the concept researchers want to measure. This paper investigates to what extent these biases impact the validity of measurements created with language models. We conduct a comparative analysis across nine group types in four datasets with three types of classification models, focusing on the robustness of models against biases and on the validity of their outputs. While we find that all types of models learn biases, the effects on validity are surprisingly small. In particular when models receive instructions as an additional input, they become more robust against biases from the fine-tuning data and produce more valid measurements across different groups. An instruction-based model (BERT-NLI) sees its average test-set performance decrease by only 0.4% F1 macro when trained on biased data and its error probability on groups it has not seen during training increases only by 0.8%.

Towards novel classification of infants’ movement patterns supported by computerized video analysis

BackgroundPositional preferences, asymmetry of body position and movements potentially indicate abnormal clinical conditions in infants. However, a lack of standardized nomenclature hinders accurate assessment and documentation of these preferences over time. Video tools offer a safe and reproducible method to analyze and describe infant movement patterns, aiding in physiotherapy management and goal planning. The study aimed to develop an objective classification system for infant movement patterns with particular emphasis on the specific distribution of muscle tension, using methods of computer analysis of video recordings to enhance accuracy and reproducibility in assessments.MethodsThe study involved the recording of videos of 51 infants between 6 and 15 weeks of age, born at term, with an Apgar score of at least 8 points. Based on observations of a recording of infant spontaneous movements in the supine position, experts identified postural-motor patterns: symmetry and typical asymmetry linked to the asymmetrical tonic neck reflex. Deviations from the typical postural-motor system were indicated, and subcategories of atypical patterns were distinguished. A computer-based inference system was developed to automatically classify individual patterns.ResultsThe following division of motor patterns was used: (1) normal patterns, including (a) typical (symmetrical, asymmetrical: variants 1 and 2); and (b) atypical (variants: 1 to 4), (2) positional preference, and (3) abnormal patterns. The proposed automatic classification method achieved an expert decision mapping accuracy of 84%. For atypical patterns, the high reproducibility of the system’s results was confirmed. Lower reproducibility, not exceeding 70%, was achieved with typical patterns.ConclusionsBased on the observation of infant spontaneous movements, it is possible to identify movement patterns divided into typical and atypical patterns. Computer-based analysis of infant movement patterns makes it possible to objectify and satisfactorily reproduce diagnostic decisions.

Visualization of Multiple-Resonance-Induced Frontier Molecular Orbitals in a Single Multiple-Resonance Thermally Activated Delayed Fluorescence Molecule.

The spatial distribution and electronic properties of the frontier molecular orbitals (FMOs) in a thermally activated delayed fluorescence (TADF) molecule contribute significantly to the TADF properties, and thus, a detailed understanding and sophisticated control of the FMOs are fundamental to the design of TADF molecules. However, for multiple-resonance (MR)-TADF molecules that achieve spatial separation of FMOs by the MR effect, the distinctive distribution of these molecular orbitals poses significant challenges for conventional computational analysis and ensemble averaging methods to elucidate the FMOs' separation and the precise mechanism of luminescence. Therefore, the visualization and analysis of electronic states with the specific energy level of a single MR-TADF molecule will provide a deeper understanding of the TADF mechanism. Here, scanning tunneling microscopy/spectroscopy (STM/STS) was used to investigate the electronic states of the DABNA-1 molecule at the atomic scale. FMOs' visualization and local density of states analysis of the DABNA-1 molecule clearly show that MR-TADF molecules also have well-separated FMOs according to the internal heteroatom arrangement, providing insights that complement existing theoretical prediction methods.

Finite element analysis with deformed shape constraints generated by laser‐scanned point clouds

AbstractThis article proposes a computational method for finite element analysis with deformed shape constraints for analyzing constructed structures to account for deformations that occur before shape measurement by a terrestrial laser scanner (TLS). In this method, point clouds obtained by a TLS are considered as a partial surface of the deformed structure. An analysis model is assumed to be created from CAD data or drawings. The analysis is performed under deformed shape constraints, namely, the deformed surface constraints or the normal vector constraints, which are generated by the point clouds. These constraints are introduced to reproduce the current displacements and stresses for the structure through the analysis. This method was applied to analyses of a plate and a desk using point clouds, which were created virtually on a computer or obtained by a TLS in the numerical examples. The results showed that this method can consider unexpected deformations that occur before laser scanning. Although the computed stresses oscillated when the scanned point cloud was used due to measurement errors and conventional point cloud processing methods, the stress values were responsive enough to indicate unnatural shapes of the deformed structure. Moreover, the oscillation was observed only in areas with constraints, whereas it was not seen in areas without constraints.

Computational methods for climate change frame analysis: Techniques, critiques, and cautious ways forward

AbstractFrame analysis is a popular methodological paradigm to investigate how climate change is reported in the media, how it is negotiated by political actors, and perceived by publics. Its scope of application extends across various academic disciplines and transcends traditional boundaries of research such as those between quantitative and qualitative methods. Recent transformations of the media landscape have a strong influence on how frame analysis is conducted and how it is used to investigate climate change communication. Online data mining and computational methods have now become increasingly mainstream to investigate discursive elements in online media. Scholars have highlighted the potential, but also the risks associated with sophisticated computational methods, such as machine learning, increasingly used in the context of frame analysis. This advanced review gathers the scientific literature on computational frame analysis for analyzing climate change communication and discusses ways of dealing with associated risks and caveats by incorporating ideas from Science & Technology Studies (STS) and other stances of critical scholarship. Recommended ways forward include combining methods, practicing theoretical interdisciplinarity, infrastructuring reflexivity in research constellations, and embracing transparency, documentation, and accessibility of methods.This article is categorized under: The Social Status of Climate Change Knowledge > Sociology/Anthropology of Climate Knowledge Perceptions, Behavior, and Communication of Climate Change > Communication Climate, History, Society, Culture > Technological Aspects and Ideas

Sequencing-based analysis of microbiomes.

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

ScHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell–cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell–cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

Simulation and analysis of Automated Generalised Interleaved Layered Execution (AGILE) process for concrete shell structures

There has been increased research and interest in new methods of automated construction. This study presents a novel computational model for an augmented and generalised version of an automated construction process that was previously tested experimentally only for vertical concrete walls. The Automated Generalised Interleaved Layer Execution (AGILE) utilises a robotic arm for the alternating deposition of formwork and concrete, aiming to facilitate the creation of free-form concrete shell structures with enhanced design and construction versatility, overcoming the constraints of previous methods. The paper proposes an FEA analysis framework for this construction methodology, evaluating its implications, limitations, and challenges via a detailed case study that includes calculating lateral pressures and displacements throughout the construction process. Moreover, it introduces new failure criteria specific to this construction method, enabling the optimisation of the design and geometry of the shell and formwork, as well as the construction process details and parameters. The proposed computational analysis method allows designers to improve the performance of a broader range of shell structures while reducing construction costs, time, complexity, and environmental impact by replacing early-stage experimental methods with finite-element analysis. In conclusion, apart from generalising the limited process and identifying its design options and parameters, this study proposes a new and straightforward computational method for analysis of the AGILE construction method in its general form in the absence of any other analysis method and where only the experimental method in one particular case was used before, identifies four different construction failure modes, and paves the way for experimental verification and calibration of this analysis method for different specific setups and applications of this general construction process.

PREDICTION AND RISK MANAGEMENT OF SPREADING FOREST PEST INFESTATIONS USING SATELLITE DATA

The article is devoted to predicting the risk of occurrence of large foci of infection in a pine forest with bark beetles, pathogenic fungi, and nematodes. The areas of disease observed on satellite images have a spotted, clustered structure of drying forest. An important statistical characteristic of the infestation structure is the power law of distribution of infestation clusters in size. Large, catastrophic events have a significant probability in processes with power laws of distributions. The given methods of computer identification and analysis of cluster distributions make it possible to form a statistical percolation model of prediction an d risk management of forest infestation based on information captured (read out) from space images. The only effective means of combating the bark beetle is sanitary felling of the forest. The sanitary cuttings area is considered a control parameter in the model. The model uses forest observation on a lattice of satellite image pixels, similar to the lattice of a percolation system. The universality of the theory is explained by the fact that it considers the interaction of elements of infection clusters, which, near the critical state of a forest ecosystem, obey a power-law distribution. The value of the power-law indicator indicates the formation of large clusters and is used in the model for the risk prediction of infestation development. In the model, risk prediction is understood as a statistical assessment of risk in the future, taking into account changes in the conditions for its manifestation. Changes are determined based on the results of satellite imagery, and the effectiveness of sanitary tree cuttings is considered. An example of a prediction of the development of forest infestations (drying) using images from the Sent inel-2 satellites is presented. Model identification methods are c onsidered, and a test verification of the model is performed. Using scale-invariant indicators of power-law distributions made it possible to abandon expensive high-precision images and replace them with images of average spatial resolution. The approach to synthesizing a prediction and risk management model from space images discussed in the article is based on the theory of self-organized criticality. The model is quite universal and can be used in space geoinformation technologies to orga- nize effective environmental management.