Post-LASIK corneas show sub-clinical differences in sensory nerves and immune cell morphodynamics.

Abstract. Sandy shoreline–sandbar systems exhibit complex variability arising from the interplay between hydrodynamic forcing and intrinsic morphological feedbacks. Using long-term satellite-derived shoreline and sandbar observations, we applied global polynomial modeling to reconstruct low-dimensional deterministic dynamics for 4 contrasting coastal sites. The resulting autonomous models reproduce key morphodynamic features, including self-sustained shoreline oscillations, shoreline–sandbar coupling, and intermittent transitions between quasi-stable configurations. Nonlinear stability analyses reveal that these systems behave as chaotic oscillators, characterized by locally divergent yet globally bounded trajectories. Energetic episodes correspond to rapid shoreline–sandbar exchanges, whereas long low-energy states reflect stable attractor confinement. Together, these results demonstrate that sandy coasts are governed by deterministic but chaotic dynamics, in which internal coupling and self-organization control both variability and finite predictability. The proposed framework offers a physically consistent and data-driven approach to characterize and compare coastal morphodynamics within a unified nonlinear dynamical perspective.

Synergistic acousto-optical profiling of morphodynamic features for smart detection of acute lymphoblastic leukemia

Abstract The number of dams is continuously increasing worldwide. In Indonesia, there are plans to construct new dams to take advantage of the abundant and available water resources, potential impoundment areas, and high terrain elevation, which are favourable conditions for multipurpose dams. However, dams also disrupt the water and sediment fluxes by regulating the flow and trapping sediment. Dams induce changes in flow and sediment transport capacity, triggering morphological changes in the downstream river reaches, which can increase the risk of erosion and floods. In Southeast Asia, including Indonesia, studies on dam-induced morphodynamic change remain limited. Therefore, this research aims to compare river morphodynamic characteristics at the river reach scale between pre- and post-dam construction downstream of the dams. This research focuses on the evolution of three rivers in Indonesia, by analysing planform changes and long-term morphological response (river width, sinuosity index, and bar numbers) using remote sensing and GIS techniques, open-access datasets, and historical data from the local authorities. Despite uncertainty, the results show that two-thirds of rivers have an increase in the sinuosity index; one of three rivers becomes narrower; the number of river bars decreased in one of two rivers. These findings highlight the variable but significant impacts of dam construction on river morphodynamics in Indonesia.

Beach morphodynamic characteristics and classifications around Hainan Island, China

We propose that current Neuroscience approaches can benefit from further integrating morphodynamics across different scales of brain organization and neural network emergent functions in complex systems. While emergence in neuroscience is commonly addressed at higher organizational levels, here we consider neuronal morphology itself as an emergent level of organization. Progressing from form-based complexity views, early models of neuronal morphogenesis, and functional approaches, we integrate cell morphology to behavior with particular relevance to the following issues: (1) Neuronal Morphological Diversity and Circuitry Function, (2) Mother-Infant Relationships, and (3) Epilepsy and Neuropsychiatric Comorbidities. The structure of neurons and their connectivity within the brain volume are morphodynamic features that emerge from dynamic interactions among morphogenetic elements, the local cell neighborhood, and synaptic connections. In turn, the emergent functions of networks are organized around a series of conceptual, experimental, and computational foundations. Complex systems neuroscience combines such data with additional high- and multiscale information to develop models organized around structure, function, and behavioral displays in both normal and pathological conditions. Here, we present and discuss examples that approximate this framework, drawing on animal models and human data. Such an integrated approach aligns with the ongoing efforts promoted by UNESCO's "UniTwin Complex Systems Digital Campus" (CS-DC) to collaboratively address open, multiscale problems in neuroscience and complex systems.

Photogrammetry is a specialized technique essential for acquiring high - resolution 2D and 3D spatial data in a non - invasive manner. Its applications are particularly significant in terrain mapping and topographic analysis, where it serves as a complementary tool for geophysical and geological surveys. Additionally, photogrammetric data play a crucial role in monitoring landslides and erosion, particularly in valley - dominated landscapes, as exemplified in this study. Furthermore, photogrammetry is widely utilized in flood risk assessment and mitigation, as well as in infrastructure planning and land use management, contributing to more informed decision - making in environmental and engineering contexts. In this study, we conducted a survey over an 83,000 - square - meter area situated in Borviz Valley ( that serves as a tributary to the Olt River ) , within Bodoc locality, in the historical region of Transylvania, Romania. The aerial photogrammetric data acquisition was carried out using a DJI Phantom 4 Pro V2.0 UAV system, supplemented by a Trimble R2 GNSS system and a network of nine ground control points (GCPs) to enhance geospatial accuracy. The dataset, consisting of 436 images, was processed using specialized photogrammetric software, such as Agisoft Metashape, along with various Topographic and GIS tools, including ESRI ArcGIS and Blue Marble Geographics Global Mapper. This processing workflow resulted in high - resolution 3D models and 2D maps, represented by a range of photogrammetric products, including Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), classified point clouds, and orthophotos (orthomosaics). By generating classified elevation models that include only the terrain class while excluding vegetation, built structures, and other anthropogenic objects, we obtained a more detailed representation of the ground surface, allowing for a more accurate depiction of the valley’s morphological characteristics. Furthermore, the orthophoto (orthomosaic), produced by integrating all photographic images acquired during the photogrammetric survey, provides a highly precise geospatial reference. This dataset can serve as a valuable resource for future survey planning across various domains such as topography, civil engineering, general infrastructure, utility construction, etc.

Abstract Alluvial bars in straight channels generally migrate downstream or can be non‐migrating but rarely migrate upstream. On the other hand, a recent long‐term field observation suggested a possibility of the upstream migration of bars. To understand this rare morphodynamic feature of alluvial bars, we herein perform non‐linear numerical simulations of alluvial bar formation and development in a straight channel. More specifically, we simulate alluvial bar dynamics under steady discharge and unsteady discharge (repeated hydrographs), wherein the bar regime retains super‐resonance condition. The result shows that initially formed, short downstream‐migrating free bars elongate, becoming much longer upstream migrating bars. The former bar elongation can be understood as a well‐observed non‐linear response of the bars, but much longer bar formation might originate from different morphodynamic features. The theoretical interpretation for this result using temporal and spatial mode linear analysis suggests a sort of forced bar formation in the upstream channel originating from nearly non‐migrating, slow‐movement bars. In other words, nearly non‐migration bars act as forcing factors, similar to an asymmetric obstacle in the channel, leading to an upstream morphodynamic influence observed under super‐resonance conditions. Subsequently, an interaction/coexistence of free‐migrating and forced‐steady bar responses may contribute to the formation of upstream‐migrating bars. These results provide new insights into the migration features of alluvial bars, but they still require a comprehensive theoretical framework to explain them. Additionally, observing upstream‐migrating bars will be an important future research, although it will be difficult due to the considerably slow migration speed predicted by the present numerical simulation.

This study examines the evolution of a beach formed along the coastline of a semi-enclosed, essentially tideless, embayment in the eastern Mediterranean Sea. The analysis revealed that the primary factors influencing its recent evolution are the terrestrial sediment influxes, current nearshore oceanographic conditions, and the existence of coastal constructions. The beach zone is exposed to waves approaching from the south with extreme values of height and period of 7 m and 4.3 s, respectively. Associated morphodynamic characteristics include a closure depth of 7 m, a breaking depth of 4.3 m, and a maximum run-up of 2.4 m. Since the mid-1900s, the shoreline has evolved through an accretional phase from 1960 to 1988, followed by a retreating phase from 1989 to 1997, except in the central part, where progradation has continued. The most recent period (1998–2017) has been relatively stable, though with a slight retreating trend. During storm events, changes to the beach are not uniform along-shore. Gross estimates of beach retreat due to sea level rise induced by climate change threaten the existence of the entire beach (for moderate and extreme IPCC Special Report Emissions Scenarios); however, this does not seem to be the case if riverine sediment influx continues.

The interaction between deep oceanic currents and an ice base is critical to accurately predict global ice melting rates, yet predictions are often affected by inaccuracies due to inadequate dynamical modelling of the ice–water interface morphology. To improve current predictive models, we numerically investigate the evolution of the ice–water interface under a subsurface turbulent shear-dominated flow, focusing on the time and length scales that govern both global and local morphological features. Based on our previous work (Perissutti, Marchioli & Soldati 2024 IntlJ.MultiphaseFlow181, 105007), where we confirmed the existence of a threshold Reynolds number below which only streamwise-oriented topography forms and above which a larger-scale spanwise topography emerges and coexists with the streamwise structures, we explore three orders of magnitude for the Stefan number (the ratio of sensible heat to latent heat). We examine its impact on ice melting and its role in shaping the interface across the two distinct morphodynamic regimes. We identify characteristic time scales of ice melting and demonstrate that the key features of ice morphodynamics scale consistently with the Stefan number and the Péclet number (the ratio of heat advection to diffusion) in both regimes. These scaling relationships can be leveraged to infer the main morphodynamic characteristics of the ice–water interface from direct numerical simulation datasets generated at computationally feasible values of Péclet and Stefan numbers, enabling the incorporation of morphodynamics into geophysical melting models and thereby enhancing their predictive accuracy.

This article explores audiovisual associations within the context of contemporary and experimental music practices, particularly focusing on sound-based music. While extensive studies exist on crossmodality in relation to traditional music genres (such as classical instrumental music), the perceptual potential of sound-based music remains an underexplored field of psychological research. In an online procedure, 152 participants were exposed to six musical excerpts from spectralism and electronic-glitch music and were asked to rate the extent to which each audio matched with six ad hoc generated black and white abstract images. Statistical analysis revealed that ratings were highly consistent across participants, indicating that they may rely on a shared set of implicit perceptual criteria rooted in Gestalt and morphodynamic features common to both auditory and visual stimuli. In particular, smoothness, continuity, numericity, symmetry, and spectrotemporal dimensions emerged as the primary factors influencing the association ratings. We discuss the implication of these findings both for crossmodal research and musicology, and suggest some directions for future research in audiovisual associations using sound-based music.

Contrast-enhanced mammography (CEM) combines morphological and functional imaging, enhancing breast cancer (BC) diagnosis. This study investigates the relationship between CEM morphodynamic features and histopathological characteristics of BC. In this prospective study, 50 female patients (mean age: 57.2 ± 13.7 years) with BI-RADS 4-5 lesions underwent CEM followed by surgical excision between December 2022 and May 2024. Low-energy and recombined CEM images were analyzed for breast composition, lesion characteristics, and enhancement patterns, while histopathological evaluation included tumor size, histotype, grade, lymphovascular invasion, and immunophenotype. Spearman rank correlation and multivariable regression analysis were used to evaluate the relationship between CEM findings and histopathological characteristics. Tumor size on CEM strongly correlated with histopathological tumor size (ρ = 0.788, p < 0.001) and was associated with high-grade lesions (p = 0.017). Non-circumscribed margins were linked to a Luminal-B subtype (p = 0.001), while high lesion conspicuity was associated with Luminal-B and triple-negative BC (p = 0.001) and correlated with larger tumors (ρ = 0.517, p < 0.001). Background parenchymal enhancement was negatively correlated with age (ρ = -0.286, p = 0.049). CEM provides critical insights into BC, demonstrating significant relationship between imaging features and histopathological characteristics. These findings highlight CEM's potential as a reliable tool for tumor size estimation, subtype characterization, and prognostic assessment, suggesting its role as an alternative to MRI, particularly for patients with contraindications.

Background/Objectives: Contrast-enhanced mammography (CEM) integrates morphological and functional imaging using contrast agents, making it a valuable tool in breast cancer (BC) diagnosis. In 2022, the Breast Imaging Reporting and Data System for CEM was introduced. This study aims to investigate the relationship between CEM morphodynamic features and the histopathological characteristics of BC. Methods: In this prospective study were consecutively enrolled, between December 2022 and May 2024, patients with BI-RADS 4 or 5 lesions undergoing CEM followed by surgical excision. Low-energy and recombined CEM images were analysed for breast composition, lesion characteristics, and enhancement patterns. Histopathological evaluation included tumor size, histotype, grade, lymphovascular invasion and immunophenotype. Spearman rank correlation and multivariable regression analysis were used to evaluate the relationship between CEM findings and histopathological characteristics. Results: Fifty female patients (mean age: 57.2 &amp;plusmn; 13.7 years) and 50 BCs were analysed. Tumor dimensions observed on CEM showed a strong correlation with histopathological tumor size (&amp;rho; = 0.788, p &amp;lt; 0.001) and were associated with grade 3 lesions (p = 0.017). Not circumscribed margins on CEM were significantly associated with the Luminal-B (p = 0.001), while high lesion conspicuity was associated with Luminal-B and triple-negative (p = 0.001) and correlated with larger tumor size (&amp;rho; = 0.517, p &amp;lt; 0.001). Background parenchymal enhancement negatively correlated with patient age (&amp;rho; = -0.286, p = 0.049). Conclusions: CEM provides critical insights into BC morphology and biology, demonstrating significant relationship between imaging features and histopathological characteristics.

Shoals and troughs are the fundamental geomorphological units of estuarine systems. However, their definition and morphodynamic characteristics, influenced by the complex dynamic environment, remain a critical challenge. This work introduces a depth–area spatial function as a quantitative criterion for the definition of shoals and troughs, while simultaneously elucidating their geodynamic implications. The Lingdingyang Bay (LDB) of the Pearl River Estuary serves as a case study. From 1901 to 2018, the LDB consisted of the West Shoal, Middle Shoal, and East Shoal and the West Trough and East Trough. The threshold depth of the LDB shifted from −5.75 m in 1901 to −4.75 m between 1964 and 2018. The depth–area distribution curve of the LDB exhibits two dominant peak depths (approximately 0 m and −2 m) within the shoal stable state, which categorizes shallow areas into high, medium and low tidal flats. The shoal–trough area ratio in the LDB, relative to the threshold depths, increased from 1901 to 1998, followed by a decline between 2008 and 2018, and culminated in a restoration to the level seen in 1901 (65% shoals and 35% troughs). Regional variations in dominant forces influencing shoal formation and evolution were observed by the vertical classification of the shoal state. The West Shoal is river dominated, the East Shoal is tide dominated, and the Middle Shoal reflects an interaction between riverine inflows and tides. Stabilized curves observed between 2008 and 2018 indicate that this estuary is progressively achieving new equilibrium states. The depth–area spatial function is useful for identifying shoals and troughs within various estuaries, which also provides a geomorphological framework for understanding the estuarine evolution and sediment dynamics.

Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

Artificial neural network based model to predict mega cusp characteristics in Hasaki, Japan

Analysis of the factors affecting erosion in the beach-dune system of Guardamar del Segura, Spain

Intravital Imaging of the Human Cornea Reveals the Differential Effects of Season on Innate and Adaptive Immune Cell Morphodynamics