Multiscale Data Analysis Research Articles

Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves.

The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.

The 3D Density Structure of the South China Sea Based on Wavelet Multi-Scale Analysis of Gravity Data and Its Tectonic Implications

Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research on 3D density structures. A comprehensive study is still needed to refine the expansion mechanism and tectonic evolution of the South China Sea. In this study, we utilized wavelet multi-scale analysis of gravity data to obtain a 3D density model of the South China Sea and discussed its tectonic evolution from the pattern of density anomalies. The inversion results show that (1) the expansion of the South China Sea caused the typical thin oceanic crust and parts of the continent–ocean transition zone may fracture due to the expansion; (2) the low-density anomaly in the upper mantle of Luzon Island may indicate partial melting or the upwelling of asthenosphere materials; and (3) the expansion of the South China Sea is influenced by multiple plate forces and uneven forces affect the distribution of high-density anomalies in the upper mantle.

Reactivation and propagation of the transbrasiliano strike-slip system in Tocantins province, central Brazil: From Ediacaran to Cretaceous

Strike-slip zones are formed by the accommodation and displacement of crustal blocks, reflecting dynamic behavior within the lithosphere. Repeated deformation in these zones can obscure earlier geological processes by creating new structural frameworks and reactivating ancient weakness zones. In central Brazil, the Transbrasiliano Lineament - a major continental-scale strike-slip system - has significantly reworked the Tocantins Province, altering key geological elements that constrain the final stages of Western Gondwana's assembly. Although several seismic events have been linked to this large structure, establishing a direct correlation remains challenging due to its crustal framework being largely obscured by Phanerozoic sedimentary basins. This study presents a multiscale analysis of geological, geomorphological and geophysical data to characterize the Transbrasiliano Lineament dynamic in central Brazil. Our findings reveal a deep crustal discontinuity associated with the Goiás-Tocantins seismic zone, characterized by low-velocity anomalies and a thinner crust within the Brazilian Platform. The complex configuration of this strike-slip system, due to successive reactivations, has displaced and reoriented previous magnetic elements, affecting the Araguaia, Brasília, and Paraguay Belts. Consequently, deformation of the Transbrasiliano Lineament has propagated from the southern Bananal Basin to the northern Paraná Basin, exhibiting a horsetail splay geometry. Structural geophysical analysis indicates that this ongoing reactivation has recorded deformation episodes from the Ediacaran-Cambrian to the Upper Cretaceous.

Extracellular matrix regulation of cell spheroid invasion in a 3D bioprinted solid tumor-on-a-chip

Tumor organoids and tumors-on-chips can be built by placing patient-derived cells within an engineered extracellular matrix (ECM) for personalized medicine. The engineered ECM influences the tumor response, and understanding the ECM-tumor relationship accelerates translating tumors-on-chips into drug discovery and development. In this work, we tuned the physical and structural characteristics of ECM in a 3D bioprinted soft-tissue sarcoma microtissue. We formed cell spheroids at a controlled size and encapsulated them into our gelatin methacryloyl (GelMA)-based bioink to make perfusable hydrogel-based microfluidic chips. We then demonstrated the scalability and customization flexibility of our hydrogel-based chip via engineering tools. A multiscale physical and structural data analysis suggested a relationship between cell invasion response and bioink characteristics. Tumor cell invasive behavior and focal adhesion properties were observed in response to varying polymer network densities of the GelMA-based bioink. Immunostaining assays and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) helped assess the bioactivity of the microtissue and measure the cell invasion. The RT-qPCR data showed higher expressions of HIF-1α, CD44, and MMP2 genes in a lower polymer density, highlighting the correlation between bioink structural porosity, ECM stiffness, and tumor spheroid response. This work is the first step in modeling STS tumor invasiveness in hydrogel-based microfluidic chips. Statement of significanceWe optimized an engineering protocol for making tumor spheroids at a controlled size, embedding spheroids into a gelatin-based matrix, and constructing a perfusable microfluidic device. A higher tumor invasion was observed in a low-stiffness matrix than a high-stiffness matrix. The physical characterizations revealed how the stiffness is controlled by the density of polymer chain networks and porosity. The biological assays revealed how the structural properties of the gelatin matrix and hypoxia in tumor progression impact cell invasion. This work can contribute to personalized medicine by making more effective, tailored cancer models.

A Stock Market Decision-Making Framework Based on CMR-DQN

In the dynamic and uncertain stock market, precise forecasting and decision-making are crucial for profitability. Traditional deep neural networks (DNN) often struggle with capturing long-term dependencies and multi-scale features in complex financial time series data. To address these challenges, we introduce CMR-DQN, an innovative framework that integrates discrete wavelet transform (DWT) for multi-scale data analysis, temporal convolutional network (TCN) for extracting deep temporal features, and a GRU–LSTM–Attention mechanism to enhance the model’s focus and memory. Additionally, CMR-DQN employs the Rainbow DQN reinforcement learning strategy to learn optimal trading strategies in a simulated environment. CMR-DQN significantly improved the total return rate on six selected stocks, with increases ranging from 20.37% to 55.32%. It also demonstrated substantial improvements over the baseline model in terms of Sharpe ratio and maximum drawdown, indicating increased excess returns per unit of total risk and reduced investment risk. These results underscore the efficiency and effectiveness of CMR-DQN in handling multi-scale time series data and optimizing stock market decisions.

Revisiting the OBS seafloor compliance signal removal with a stationarity and stacking-based approach: the BRUIT-FM toolbox

Summary This study focuses on improving the seafloor compliance noise removal method, which relies on estimates of the compliance transfer function frequency response (the deformation of the seafloor under long-period pressure waves). We first propose a new multi-scale deviation analysis of broadband ocean bottom seismometer data to evaluate stationarity properties that are key to the subsequent analysis. We then propose a new approach to removing the compliance noise from the vertical channel data, by stacking daily estimated transfer function frequency responses over a period of time. We also propose an automated transient event detection and data selection method based on a cross-correlation criterion. As an example, we apply the method to data from the Cascadia Initiative (network 7D2011). We find that the spectral extent of long-period forcing waves varies significantly over time so that standard daily transfer function calculation techniques poorly estimate the transfer function frequency response at the lowest frequencies, resulting in poor denoising performance. The proposed method more accurately removes noise at these lower frequencies, especially where coherence is low, reducing the mean deviation of the signal in our test case by 27 % or more. We also show that our calculated transfer functions can be transfered across time periods. The method should allow better estimates of seafloor compliance and help to remove compliance noise at stations with low pressure-acceleration coherence. Our results can be reproduced using the BRUIT-FM Python toolbox, available at https://gitlab.ifremer.fr/anr-bruitfm/bruit-fm-toolbox.

A Review of Research Progress on the Impact of Urban Street Environments on Physical Activity: A Comparison between China and Developed Countries

Creating healthy street environments to encourage physical activity is an effective strategy against non-communicable diseases exacerbated by rapid urbanization globally. Developing countries face more significant health challenges than developed ones. However, existing research predominantly focuses on the perspective of developed countries. To address the health challenges in developing nations, studies should not only draw on the findings from developed countries but also clearly define unique research processes and pathways. Consequently, this study conducts a comparative analysis between China, representing developing countries, and developed nations, using databases like China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) and tools such as Citespace, Bicomb, and Statistical Package for the Social Sciences (SPSS) to explore research hotspots, developmental trajectories, thematic categories, and trends. The findings reveal a shift in developed countries from macro-material to micro-environmental elements under multidisciplinary scrutiny, while future topics may include street space evaluations and psychological healing. In China, research has been dominated by different disciplines at various stages, starting with medical attention to chronic disease prevention, which then shifted to traffic engineering’s focus on constructing green travel environments, and finally expanded to disciplines like landscape architecture examining the impact of street environment elements on pedestrian behavioural perceptions. Future themes will focus on promoting elderly health and urban health transport systems. Generally, research in developed countries exhibits a “bottom-up” approach, with practical issues at a “post-evaluation” stage, primarily based on the “socio-ecological model” and emphasizing multidisciplinary collaboration. Chinese research shows a “top-down” characteristic, driven by national policies and at a “pre-planning” stage, integrating theories such as Maslow’s hierarchy of needs and attention restoration theory, with relatively loose disciplinary cooperation. Overall, research is shifting from macro to human-centric scales and is progressively utilizing multi-source and multi-scale big data analysis methods. Based on this, future research and development recommendations are proposed for developing countries, with China as a representative example.

The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis

The Neotectonic Deformation of the Eastern Rif Foreland (Morocco): New Insights from Morphostructural Analysis

Coastal Water Pollution Characterization: Enhanced Situational Awareness Through Multiscale Data Acquisition and Analysis

Coastal Water Pollution Characterization: Enhanced Situational Awareness Through Multiscale Data Acquisition and Analysis

Target recognition and detection system based on sensor and nonlinear machine vision fusion

Abstract In order to realize the automatic detection system of electric sensor, a method based on sensor and nonlinear machine vision is proposed. Aiming at complex scenes and dynamic changes in target recognition and detection in large-scale industrial field, a target recognition and detection system based on the fusion of vision sensor and nonlinear machine vision is proposed. The system introduces nonlinear features and uses deep neural network to realize multi-scale analysis and recognition of image data on the basis of traditional machine vision. The system uses C++ language development and has a good user interface. The photoelectric sensor weld image is collected by machine vision technology, the target area of the image is detected by Gaussian model, the feature points of the target area are extracted by combining Hessian matrix, the extracted feature points are input into the quantum gate neural network model, and the recognition results are obtained. The simulation results show that the author’s method has the highest value among the three test indicators, with the highest accuracy rate of 97%, the highest recall rate of 98%, and the highest F1 value of 94. The time consumed by the author’s method for automatic identification of photoelectric sensor welding is within 6 s, the time spent on the film wall recognition method for automatic identification of photoelectric sensor welding is within 20 s, and the time spent by the feature extraction and identification method for automatic identification of photoelectric sensor weld is within 22 s. It has been proven that the method based on the fusion of sensors and nonlinear machine vision can achieve an automatic recognition and detection system for electrical sensor welds. The object detection and recognition method proposed in this article can be applied to dynamic changes and complex scenes in various complex backgrounds and has a good application prospect. The system proposed in this article has some limitations, such as the algorithm in the calculation accuracy, real-time, and other aspects that have room for improvement.

A GPU Parallel Algorithm for Computing Morse-Smale Complexes.

The Morse-Smale complex is a well studied topological structure that represents the gradient flow behavior between critical points of a scalar function. It supports multi-scale topological analysis and visualization of feature-rich scientific data. Several parallel algorithms have been proposed towards the fast computation of the 3D Morse-Smale complex. Its computation continues to pose significant algorithmic challenges. In particular, the non-trivial structure of the connections between the saddle critical points are not amenable to parallel computation. This paper describes a fine grained parallel algorithm for computing the Morse-Smale complex and a GPU implementation (gmsc). The algorithm first determines the saddle-saddle reachability via a transformation into a sequence of vector operations, and next computes the paths between saddles by transforming it into a sequence of matrix operations. Computational experiments show that the method achieves up to 8.6× speedup over pyms3d and 6× speedup over TTK, the current shared memory implementations. The paper also presents a comprehensive experimental analysis of different steps of the algorithm and reports on their contribution towards runtime performance. Finally, it introduces a CPU based data parallel algorithm for simplifying the Morse-Smale complex via iterative critical point pair cancellation.

Land cover changes mapping of the west-Algerian territory: a multiscale data analysis for the estimation of the sustainable goal 15.3.1

Land cover changes mapping of the west-Algerian territory: a multiscale data analysis for the estimation of the sustainable goal 15.3.1

A prospect-regret theory-based three-way decision model with intuitionistic fuzzy numbers under incomplete multi-scale decision information systems

As a new data representation in the big data era, a multi-scale decision information system (MSDIS) realizes “multi-level, multi-angle and multi-view” evaluations in various problems. However, most of existing multi-scale data analysis models are built for complete information systems (CISs), whereas the research on incomplete multi-scale decision information systems (I-MSDISs) is not perfect. Additionally, the irrational behavior of one decision-maker (DM) often has an impact on decision outcomes. On this basis, this paper develops a prospect-regret theory-based three-way decision (3WD) model with intuitionistic fuzzy numbers (IFNs) under an I-MSDIS, which is abbreviated as PR-3WD-I-MSDIS. Specifically, we first select the optimal scale combination of I-MSDISs by the matrix theory to complete the information extraction of incomplete optimal sub-systems. Then, we further propose an attribute weight evaluation strategy under the incomplete optimal subsystem, which fully considers the neighborhood information of each object. Subsequently, the concept and calculation method of aggregated weighted fuzzy conditional probabilities are constructed from the perspective of intuitionistic fuzzy sets. Furthermore, a prospect-regret theory-based relative profit function is proposed, which sufficiently takes into account the loss and utility in decision-making processes. Finally, a novel three-way classification and ranking method is developed for solving incomplete multi-scale problems. The experimental results on real-world datasets demonstrate that the PR-3WD-I-MSDIS model achieves excellent performance.

Geothermal Mechanism and Reservoir Characterization with Multiscale Analysis of the Potential Field and MT Profile in the Yanqing-Huailai Basin, North China

Abstract Geothermal energy has attracted increasing attention worldwide due to its abundant reserves, stability, and sustainability. Based on the survey of geothermal resources in the Yanqing-Huailai Basin, this paper conducts comprehensive data study, demonstrates geothermal distribution and multiscale characteristics, and constructs the geothermal mechanism and conceptual model to promote regional geothermal energy development and utilization. Our study proposes to characterize geothermal reservoirs using geophysical data ranging from the representative geothermal field scale to the basin scale. Hydrochemical samples were also collected. According to the results of multiscale analysis and interpretation, combined with field geological and geothermal investigations, it is concluded that the Dahaituo pluton is composed of acid granite and monzonite granite, with low gravity and high magnetic anomaly. Its intrusion and development are responsible for the local geothermal anomaly. Multiscale analysis and source depth estimation of potential field data and MT profile interpretation were carried out. The regional thermal structure and reservoir characteristics were demonstrated from the deep to the shallow. In addition, according to the regional geological background and geophysical interpretation, a three-dimensional geological model is constructed to characterize the distribution law and genetic mechanism of geothermal anomalies. The study shows that strong Cenozoic tectonic movement and the intrusion and development of rock mass are the main factors causing local geothermal anomaly. The development of regional deep and major faults and extensional structures, as well as the channels and space formed by the breaking of rocks at the intersection of faults, provides advantageous conditions for the upwelling, migration, and convergence of geothermal fluids. The recharge of surface water in the intermountain basin and thermal convection drive the circulation of the geothermal system.

Multiscale analysis of count data through topic alignment.

Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics $K$. Since there is no definitive way to choose $K$ and since a true value might not exist, we develop a method, which we call topic alignment, to study the relationships across models with different $K$. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when $K$ increases. This strategy gives more insight into the process of generating the data than choosing a single value of $K$ would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto.

Lifting Scheme for Streamflow Data in River Networks

Abstract This paper presents a new multiscale method for analysing water pollutant data located in river networks. The main idea of the proposed method is to adapt the conventional lifting scheme, reflecting the characteristics of streamflow data in the river network domain. Due to the complexity of the data domain structure, it is difficult to apply the lifting scheme to the streamflow data directly. To solve this problem, we propose a new lifting scheme algorithm for streamflow data that incorporates flow-adaptive neighbourhood selection, flow proportional weight generation and flow-length adaptive removal point selection. A nondecimated version of the proposed lifting scheme is also provided. The simulation study demonstrates that the proposed method successfully performs a multiscale analysis of streamflow data. Furthermore, we provide a real data analysis of water pollutant data observed on the Geum-River basin compared to the existing smoothing method.

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = −0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = −0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.

Single-molecule study of VEGFR-2 spatiotemporal organization and activation on the surface of live endothelial cells

The dynamic organization of cell surface receptors plays an important role in signaling. Here we investigated the spatiotemporal organization of VEGF receptor-2 (VEGFR-2), a critical pro-angiogenic receptor tyrosine kinase in live, microvascular endothelial cells. Our study strategy combined live-cell single-molecule imaging of endogenous VEGFR-2 with computational image analysis and multiscale data analysis. We found that VEGFR-2, even in its basal, unstimulated state, possesses diffusion heterogeneity, with a mobile subpopulation and a subpopulation of restricted mobility, and assembly state heterogeneity, where receptors can be organized as monomeric or non-monomeric.

The scale effects of landscape variables on landscape experiences: a multi-scale spatial analysis of social media data in an urban nature park context

The roles of landscape variables regarding the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships between landscape variables and categorized nature experiences have not been adequately studied from an experimental perspective. This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: (1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and how are these experiences associated with specific landscape variables? (2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences? Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulted from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences. All considered landscape variables were identified as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context- and place-specific relationships as well as the limited applicability of simple approaches that are incapable of accounting for spatial heterogeneity and scale. The spatial effect of landscape variables on landscape experiences was clarified and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.

The Human Disease Ontology 2022 update.

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources.