Interactive Exploration Research Articles

To investigate how rigid head motion interacts with 3D MRI k-space sampling strategies and to introduce motion-sampling plots as a framework for predicting motion artifacts. We evaluated a range of motion-sampling combinations across three sampling trajectories (Cartesian, stack-of-stars, kooshball) in both simulation and in vivo. Experiments included shifting motion states in k-space, changing the direction of motion with regards to the sampling, and varying the magnitude of motion. In vivo experiments were conducted on healthy volunteers mimicking patient motion while wearing a real-time pose-tracking device. Motion-sampling plots were used to map motion states directly onto k-space and assess their relationship to artifact appearance. Nine categories of motion artifacts were identified. The severity and nature of artifacts were found to depend heavily on the k-space distribution of motion states. Motion-sampling plots were seen to work as guides in predicting artifact appearance. In vivo findings supported simulation results. Artifacts were especially pronounced when motion discontinuities occurred near the center of k-space or aligned with slow phase-encoding directions. Motion-sampling plots offer an effective way to visualize and interpret motion artifacts in 3D MRI, providing insight beyond traditional motion-time plots. This framework enables systematic evaluation of motion robustness and can guide the development and validation of motion correction techniques. We propose practical recommendations for motion experiment design to improve reproducibility and benchmarking in MRI research.

In recent years, with the implementation of China’s “comprehensive two-child” policy, the number of two-child families has increased. As one of the most enduring and inevitable social relationships in an individual’s life course, sibling relationships have a significant impact on individual development and family stability. Types of sibling relationships include warm sibling relationships, conflictual sibling relationships, and relationships where warm and conflictual sibling relationships coexist. The influencing factors of its formation and development include individual factors, family factors, and social factors. Existing research often focuses on the impact mechanism of a single factor, such as parent-child relationships or sibling age gaps, while exploration of multi-factor interactions remains insufficient. This paper attempts to explore the interaction of various influencing factors at the individual, family, and social levels based on existing literature. Future research should incorporate the characteristics of Chinese culture for localized studies; Further reveal the interaction between different influencing factors of sibling relationships and their mechanisms, and construct a systematic model of influencing factors for sibling relationships.

Bzier curves have proven to be one of the most powerful tools in geometric design and engineering due to their simplicity, efficiency, and flexibility. This paper discusses the mathematical theory and algorithms behind the construction of Bzier curves, including the classical de Casteljau method and cubic spline extensions, and introduces an online Streamlit-based tool for visualizing their applications. By generalizing these algorithms, our framework provides enhanced shape adjustability in curve design and facilitates interactive exploration of their properties. The tool demonstrates Bzier curves wide-ranging applications in computer-aided design, generative digital art, airfoil design optimization, and motion planning for autonomous systems. These examples highlight how a single mathematical framework can unify diverse domains, reducing fragmentation between theory and practice. In addition to illustrating established algorithms, we show how Bzier-based parameterizations enable intuitive geometric manipulation and maintain stability under affine transformations. The educational utility of our platform also helps students and engineers bridge the gap between abstract mathematics and real-world engineering challenges. Future extensions of this work include expanding to three-dimensional curve and surface generation, incorporating optimization methods for design automation, and applying the framework to real-time simulations. Overall, our study highlights the enduring importance of Bzier curves as a versatile and practical tool that connects mathematics, engineering, and digital creativity.

This study aims to develop and evaluate a differentiated educational game to enhance junior high school students’ understanding and engagement in learning three-dimensional geometry. Using a Research and Development (R&D) approach with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation), a game titled “Five Dragon Balls” was designed with RPG Maker MV to accommodate diverse learning styles—visual, auditory, and kinesthetic. The participants were 25 eighth-grade students from a public junior high school in Cirebon Regency, Indonesia. Data were collected through observation, interviews, and documentation, then analyzed using inductive thematic analysis. Expert validation indicated high content and media validity (88.5% and 87.3%, respectively). Classroom implementation revealed improvements in students’ conceptual understanding, motivation, and participation. Visual learners benefited from 3D representations, auditory learners from narration and cues, and kinesthetic learners from interactive exploration. The results demonstrate that a differentiated game-based learning approach can effectively support inclusive, engaging, and conceptually meaningful mathematics learning. This study contributes a practical framework for integrating differentiated instruction principles into digital learning environments that align with the Merdeka Curriculum and 21st-century educational goals.

The occurrence of gain of function mutations in STAT5B has been associated to survival, and drug resistance in Leukemia. In silico screening of compounds having inhibitory potential towards mutated proteins, can be helpful in the development of specific inhibitors. This study was designed to screen selected JAK-STAT mutations in leukemia patients and virtual exploration of molecular interaction of potential inhibitors with their mutated products. In total 276 patients were randomly recruited for this study. Demographic and clinical data were summarized. The genetic status of JAK1V623A, JAK2 S473 and STAT5BN642H were screened through allele specific PCR. In-silico analysis was performed on wild type and mutant protein sequences retrieved from Protein databank. The ligands and protein were prepared through standard protocols, and docking was performed through Auto Dock Vina 1.2.0. Acute lymphoblastic leukemia comprises 70% of the total patients. Male to female ratio was 3:1. All the patients were homozygous for JAK1V623A, JAK2 S473 major allele. However, 6 patients (5 male, 1 female) with ALL were STAT5BN642H+. The molecular docking of the ligands to wild type and STAT5BN642H+revealed that AC- 4-130, Pimozide, Indirubin and Stafib-2 have higher but differential docking affinities for SH2-domain of both normal and mutated STAT5B. However, AC-4-130 has a higher affinity for wild type and Stafib-2 has stable molecular interaction with STAT5BN642H+. The aggressive form of pediatric leukemia, carrying STAT5BN642H+ mutation is identified in the studied population. It is predicted that AC-14-30 and stafib-2 have potential for inhibition of constitutively active STAT5B if optimized for use in combination therapy.

Boron-containing compounds (BCC) are attracting attention in drug design. Certain chemical features invite the exploration of efficacious interactions on known and potential drug targets for human use. The objective of this study is to analyze the reported crystal structure studies to determine trends resulting from the inclusion of boron atoms in potential drugs. Published data in the Protein Data Bank (PDB) with at least one BCC were analyzed; both ligands and targets were analyzed to describe the inferred or reported biological activity and the potential application as a drug in the treatment of human diseases. Data from the PDB indicated targets for certain infectious diseases and cancers; however, potential treatments may extend to many other human pathologies as a consequence of the careful analysis of BCCs with proteins. All classes of enzymes and receptors have been crystallized with BCCs as ligands with most complexes demonstrating interactions in the regions known as relevant to protein function. The number of crystallized BCC-proteins complexes is increasing, and the variability of proteins expands the possibilities of medical applications. Currently, most systems are related to cancer growth and treatment, but deeper analysis may expand BCC utility and efficacy to many other chronic and degenerative diseases.

Tandem synthesis of zinc tetrazolate complexes via [3 + 2] cyclo-addition at ambient condition and exploration of noncovalent interactions in their solid state structures

Unraveling the mechanisms of dual-permeability flow in weakly cemented sandstones: an in-depth exploration of matrix-fracture interactions

Integrated Tn-seq and MAGE-assisted rapid genome engineering targeting in Escherichia coli.

AUDIT: An open-source Python library for AI model evaluation with use cases in MRI brain tumor segmentation.

RNA therapeutics are transforming precision medicine, yet a dedicated resource for validated agents has been lacking. Here, we introduce theRNA (https://therna.renlab.cn/), the first comprehensive database of 6860 experimentally validated RNA therapeutics targeting 1310 diseases in human or animal models. Curated from 11 126 peer-reviewed studies, theRNA focuses exclusively on functional therapeutic RNAs, including messenger RNA, small interfering RNA, microRNA, long noncoding RNA, circular RNA, short hairpin RNA, antisense oligonucleotides, aptamers, and CRISPR-related RNAs. Each entry provides extensive annotations on delivery modalities, efficacy readouts, and clinical potential. A user-friendly interface enables rapid searching and interactive exploration. As the first dedicated database for functional RNA therapeutics, theRNA aims to accelerate therapy development and deepen mechanistic understanding of disease and treatment.

Introduction Real-world networks possess complex, higher-order structures that are not captured by traditional pairwise analysis methods. Q-analysis provides a powerful mathematical framework based on simplicial complexes to uncover and quantify these multi-node interactions. However, its adoption has been limited by a lack of accessible software tools. Methods We introduce a comprehensive Python package that implements the core methodology of Q-analysis. The package enables the construction of simplicial complexes from graphs or simplex lists and computes a suite of descriptive metrics, including structure vectors (FSV, SSV, TSV) and topological entropy. It features high-performance routines, integration with scikit-learn for machine learning workflows, and tools for statistical inference, such as permutation tests. Results We demonstrate the package’s capabilities through a simulation study, revealing distinct higher-order topological signatures in scale-free versus configurational networks despite identical degree distributions. Application to the DBLP co-authorship dataset uncovered the evolution of collaborative structures over three decades, showing increased collaboration scale and shifts in higher-order connectivity patterns. Finally, in a network physiology application, the package identified significant disruptions in the higher-order organization of fMRI-derived brain networks in Major Depressive Disorder (MDD), characterized by a loss of high-dimensional functional components and increased fragmentation. Discussion The developed package makes Q-analysis accessible to a broad research audience, facilitating the exploration of higher-order interactions in complex systems. The presented applications validate its utility across diverse domains, from social networks to neuroscience. By providing an open-source tool, this work bridges a gap in network science, enabling quantitative analysis of the intricate, multi-node structures that define real-world networks.

Spatial transcriptomics enables detailed mapping of gene expression within tissues, revealing spatial organization of cellular and molecular processes. However, generating such data is costly and technically challenging, and analysis requires advanced bioinformatics skills. Although public datasets are growing, existing databases offer limited tools for interactive exploration and cross-sample comparison. Here, we introduce DeepSpaceDB (www.deepspacedb.com), a next-generation spatial transcriptomics database designed to address these challenges. The current version of DeepSpaceDB focuses on 10X Genomics Visium samples, ensuring higher-quality analyses and enhanced tools. This distinguishes it from databases that prioritize broad platform coverage over functionality. Emphasizing interactivity and advanced analytics, DeepSpaceDB enables flexible exploration of spatial transcriptomics data. Users can interactively compare gene expression across regions within or between tissue slices, such as between hippocampal areas of an Alzheimer's model mouse and a control. The database also offers quality indicators, database-wide trends, and interactive visualizations like zoomable plots and hover-based info. Moreover, these functions are not restricted to samples in our database but can also be applied to samples uploaded by users. Combining advanced tools with interactive features, DeepSpaceDB is a powerful resource for spatial transcriptomics, enabling deeper insights into tissue organization and disease biology.

Abstract. The physical and chemical properties of biomass burning (BB) smoke particles vary with fuel type and burning conditions, greatly affecting their impact on climate and air quality. However, the factors affecting smoke particle properties are not well characterized on a global scale, and the factors controlling their evolution during transport are not well constrained. From observations of the Multi-Angle Imaging Spectrometer (MISR) instrument aboard NASA's Terra satellite, smoke aerosol optical depth (AOD) can be retrieved, along with constraints on near-source plume vertical extent, smoke age, and particle size, shape, light-absorption, and absorption spectral dependence. Previous work demonstrated the robust, statistical characterization of BB particles across Canada and Alaska using MISR and other remote sensing data. Here we expand upon this work, studying over 3600 wildfire plumes across Siberia. We leverage the MISR Research Aerosol (RA) algorithm to retrieve smoke particle properties and the MISR Interactive Explorer (MINX) tool to retrieve plume heights and the associated wind vectors. These results are compared statistically to available observations of fire radiative power (FRP), land cover characteristics, and meteorological information. Correlations appear between the retrieved smoke particle properties, smoke age, local ambient conditions, and fuel type, allowing us in many cases to infer the dominant aging mechanisms and the timescales over which they occur. Specifically, we find that plumes located in areas with higher peat content are subject to less oxidation and condensation/hydration compared with other plume types (e.g., forest and grassland), and are predominantly affected by dilution instead.

Bacterial small RNA are important context-sensitive regulators of gene expression, especially in highly pathogenic bacteria, often controlling virulence. The number of predicted small RNA (sRNA) entries in public repositories has grown exponentially, contrasting with the linear growth of functionally validated sRNAs. While there are databases maintaining sRNA records from single bacterial species or taxonomic groups, a central repository of bona fide sRNAs for all bacteria with evidence, alignment, and RNA expression information is missing. Such a repository is a critical starting point for both wet lab biologists validating sRNA function as well as bioinformaticians creating new models for sRNA prediction. In this paper, we hand-curate 1117 articles from the literature to find 746 sRNAs that have been confirmed by northern blotting,quantitative polymerase chain reaction (qPCR), mutagenesis, or other functional validation methods. We map these sRNA sequences to QC-filtered bacterial genomic assemblies from NCBI, obtaining 3.8 million hits from 44789 chromosomes and 10884 plasmids. Finally, we also quantify these sRNAs in a filtered subset of 5292 isolates with available RNA-seq data from the Sequence Read Archive. The bona fide set, alignment, and expression information is available for download and interactive exploration at https://web.ccb.uni-saarland.de/smallbarna/.

Abstract. The increasing volume and complexity of multimodal spatio-temporal data requires advanced approach for data exploration, integration and interpretation. This paper presents a flexible and extensible dashboard framework designed to support data-driven decision-making through interactive visualisation, exploration and analysis. The dashboard allows users to interpret structured data such as GNSS, IMU or temperature measurements as well as unstructured data types such as image files and contextual metadata. The dashboard follows the principle of combining data with reference data and display the process from raw to processed data. The concept is developed both for analysts and developers who need to identify suitable methods for analysing data and for subsequent debugging during the project. It supports analytical tasks such as pattern recognition, the integration of data sets and the validation of analysis results. By using InfluxDB for time series data management and Grafana for dynamic visualisation, the architecture ensures high scalability and responsiveness even with complex data sets. A use case from rail infrastructure monitoring shows how the dashboard facilitates data-driven insights and improves interpretability by linking multiple data perspectives. The integration of raw data, processed results and planned information enables users to align analysis results with operational objectives. This paper presents a reusable dashboard approach that improves decision-making and visual understanding of multimodal spatio-temporal data in applied cartographic and monitoring contexts.

Nonlinear dimensionality reduction (NDR) techniques are widely used to visualize high-dimensional data. However, they often lack explainability, making it challenging for analysts to relate patterns in projections to original high-dimensional features. Existing interactive methods typically separate user interactions from the feature space, treating them primarily as post-hoc explanations rather than integrating them into the exploration process. This separation limits insight generation by restricting users' understanding of how features dynamically influence projections. To address this limitation, we propose a bidirectional interaction method that directly bridges the feature space and the projections. By allowing users to adjust feature weights, our approach enables intuitive exploration of how different features shape the embedding. We also define visual semantics to quantify projection changes, enabling structured pattern discovery through automated query-based interaction. To ensure responsiveness despite the computational complexity of NDR, we employ a neural network to approximate the projection process, enhancing scalability while maintaining accuracy. We evaluated our approach through quantitative analysis, assessing accuracy and scalability. A user study with a comprehensive visual interface and case studies demonstrated its effectiveness in supporting hypothesis generation and exploratory tasks with real-world data. The results confirmed that our approach supports diverse analytical scenarios and enhances users' ability to explore and interpret high-dimensional data through interactive exploration grounded in the feature space.

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, with nearly 18–20 million deaths annually. Conventional pharmacological and surgical therapies have improved survival but are often associated with side effects, high costs, and limited long-term efficacy. Nutraceuticals—bioactive compounds derived from dietary sources such as omega-3 fatty acids, phytosterols, polyphenols, vitamins, probiotics, and herbal bioactives—have emerged as promising adjuncts for the prevention and management of CVD. These agents exert cardioprotective effects through diverse mechanisms, including lipid-lowering, antioxidant, anti-inflammatory, anti-thrombotic, and endothelial-protective pathways. Clinical trials such as GISSI-Prevenzione, REDUCE-IT, PREDIMED, and Q-SYMBIO provide strong evidence supporting the efficacy of specific nutraceuticals, particularly omega-3 fatty acids, plant sterols, Coenzyme Q10, and polyphenols, in reducing cardiovascular risk and improving outcomes in patients with heart disease. Furthermore, plant-based diets rich in fruits, vegetables, legumes, and whole grains—naturally enriched with nutraceuticals—demonstrate significant protective benefits against CVD progression. However, challenges remain regarding variability in supplement quality, bioavailability, and the need for standardized dosing. Future directions include integration of nutraceuticals into precision nutrition, exploration of gut microbiota interactions, and development of novel delivery systems to enhance clinical effectiveness. Overall, nutraceuticals represent a cost-effective, multi-targeted, and accessible strategy that complements conventional therapies, offering a promising era in the prevention and treatment of cardiovascular disease. Keywords: Nutraceuticals; Cardiovascular disease; Omega-3 fatty acids; Polyphenols; Probiotics; Functional foods; Antioxidants; Precision nutrition.

Developing effective, versatile, and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications. Despite progress in interaction-oriented sensing skins, limitations remain in unit-level reconfiguration, multiaxial force and motion sensing, and robust operation across dynamically changing or irregular surfaces. Herein, we develop a reconfigurable omnidirectional triboelectric whisker sensor array (RO-TWSA) comprising multiple sensing units that integrate a triboelectric whisker structure (TWS) with an untethered hydro-sealing vacuum sucker (UHSVS), enabling reversibly portable deployment and omnidirectional perception across diverse surfaces. Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer, the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°, while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption. Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios, including teleoperation, tactile diagnostics, and robotic autonomous exploration. Overall, RO-TWSA presents a versatile and high-resolution tactile interface, offering new avenues for intelligent perception and interaction in complex real-world environments.

Ischemic Stroke (IS) represents the most prevalent subtype of cerebrovascular disease, characterized by complex pathophysiological mechanisms that remain inadequately characterized, particularly concerning mitochondrial dysfunctions. These mitochondrial impairments are increasingly recognized as contributory factors in IS pathogenesis, emphasizing the need for further investigation into the underlying molecular mechanisms involved. In this study, we integrated transcriptomic datasets from the Gene Expression Omnibus (GEO) with the comprehensive MitoCarta3.0 mitochondrial proteome inventory to elucidate the role of dysregulated Mitochondrial-Related Genes (MRGs) in IS. We employed an advanced bioinformatics and machine learning pipeline, incorporating differential expression profiling alongside network-based prioritization using CytoHubba. Rigorous feature selection was conducted through LASSO regression, Support Vector Machine (SVM), and Random Forest (RF) algorithms to derive a robust core MRG signature. Our methodology included training and validation cohorts to construct diagnostic models, which were critically evaluated via Receiver Operating Characteristic (ROC) curves, nomograms, and calibration analyses. Our analysis identified a seven-gene signature comprising DNAJA3, ACSL1, HSDL2, ECHDC2, ECHDC3, ALDH2, and PDK4, which demonstrated significant correlation with activated CD8+ T-cell and natural killer cell infiltration. Furthermore, integrative network analyses revealed intricate regulatory interactions among MRGs, microRNAs, and transcription factors. Notably, drug-target predictions indicated Bezafibrate as a promising therapeutic agent for modulating mitochondrial homeostasis in the context of IS. These findings offer a novel framework for ischemic stroke diagnosis and therapy, yet their computational derivation underscores the need for thorough experimental validation of MRGs and drug candidates, along with the integration of diverse clinical data to confirm their real-world applicability. Our findings underscore mitochondrial dysfunction not only as a critical factor in IS pathogenesis but also as a viable therapeutic target. The identified MRG signature presents potential for clinical application in diagnostic and pharmacological strategies aimed at ameliorating ischemic injury. This study highlights the translational significance of systems biology approaches within cerebrovascular medicine, warranting further mechanistic exploration of mitochondrial-immune interactions in stroke pathology.