3D Context Research Articles

Three-dimensional dynamics of mesothelin-targeted CAR.CIK lymphocytes against ovarian cancer peritoneal carcinomatosis

Intraperitoneal cellular immunotherapy with CAR-redirected lymphocytes is an intriguing approach to target peritoneal carcinomatosis (PC) from ovarian cancer (OC), which is currently evaluated in clinical trials. PC displays a composite structure with floating tumor cells within ascites and solid-like masses invading the peritoneum. Therefore, a comprehensive experimental model is crucial to optimize CAR-cell therapies in such a peculiar environment. Here, we explored the activity of cytokine-induced killer lymphocytes (CIK), redirected by CAR against mesothelin (MSLN-CAR.CIK), within reductionistic 3D models resembling the structural complexity of both liquid and solid components of PC. MSLN-CAR.CIK effectively killed and were functionally efficient against OC targets. In a “floating-like” 3D context with floating OC spheroids, both tumor localization and killing by MSLN-CAR.CIK were significantly boosted by fluid flow. In a “solid-like” context, MSLN-CAR.CIK were recruited through the extracellular matrix on embedded tumor aggregates, with variable kinetics depending on the effector-target distance. Furthermore, MSLN-CAR.CIK penetrated the inner levels of OC spheroids exerting effective tumor killing. Our findings provide currently unknown therapeutically relevant information on intraperitoneal approaches with CAR.CIK, supporting further developments and improvements for clinical studies in the context of locoregional cell therapy approaches for patients with PC from OC.

Ordinal information, but not metric information, matters in binding feature with depth location in three-dimensional contexts.

A basic function of human visual perception is the ability to recognize and locate objects in the environment. It has been shown that two-dimensional (2D) location can reliably bias judgments on object identity (spatial congruency bias; Golomb et al., 2014), suggesting that 2D location information is automatically bound with object features to induce such a bias. Although the binding problem of feature and location has been vigorously studied under various 2D settings, it remains unclear how depth location can be bound with object features in a three-dimensional (3D) setting. Here we conducted five experiments in various 3D contexts using the congruency bias paradigm, and found that changes of object's depth location could influence perceptual judgments on object features differently depending on whether its relative depth order with respect to others changed or not. Experiments 1 and 2 showed that the judgments on an object's color could be affected by changes in its ordinal depth, but not by changes in its absolute metric depth. Experiment 3 showed that the bias was asymmetric-changes in an object's color did bias the judgments on metric-depth location, but not if its depth order had changed. Experiments 4 and 5 investigated whether these findings could be generalized to a peripersonal near space and a large-scale far space, respectively, using more ecological virtual environments. Our findings suggest that ordinal depth plays a special role in feature-location binding: an object may be automatically bound with its relative depth relation, but not with its absolute metric-depth location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

DyConfidMatch: Dynamic thresholding and re-sampling for 3D semi-supervised learning

Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from underrepresented classes. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation. Through extensive experiments in 3D SSL, our method surpasses state-of-the-art counterparts in classification and detection tasks, highlighting its effectiveness in tackling data imbalance. This approach presents a significant advancement in SSL for 3D datasets, providing a robust solution for data imbalance issues.

Development and evolution of Drosophila chromatin landscape in a 3D genome context

Little is known about how the epigenomic states change during development and evolution in a 3D genome context. Here we use Drosophila pseudoobscura with complex turnover of sex chromosomes as a model to address this, by collecting massive epigenomic and Hi-C data from five developmental stages and three adult tissues. We reveal that over 60% of the genes and transposable elements (TE) exhibit at least one developmental transition of chromatin state. Transitions on specific but not housekeeping enhancers are associated with specific chromatin loops and topologically associated domain borders (TABs). While evolutionarily young TEs are generally silenced, old TEs more often have been domesticated as interacting TABs or specific enhancers. But on the recently evolved X chromosome, young TEs are instead often active and recruited as TABs, due to acquisition of dosage compensation. Overall we characterize how Drosophila epigenomic landscapes change during development and in response to chromosome evolution, and highlight the important roles of TEs in genome organization and regulation.

Pseudo-dynamic stability analysis of 3D rock slopes considering tensile strength-modified Hoek–Brown failure criterion: Seismic UBLA implementations

The Hoek-Brown (HB) criterion has found extensive application in the stability analysis of intact or jointed rock slopes, in both two-dimensional (2D) and three-dimensional (3D) contexts. In the early days, the assessment of Hoek-Brown slopes rarely incorporated tensile strength until, recently, limited studies examined the tension-controlled toppling failure at the sliding head. Nevertheless, the adapted tensile strength, which is extrapolated from the HB envelope the tensile regime, has been found overestimate experimental results. Thus, for more realistically representing the influence of rock tensile strength, the present study introduces a novel procedure for assessing the seismic stability of 3D HB slopes. In this study, the tensile strength-modified HB envelopes considering cut-off effects are derived in the principal stress space and Mohr space. To assess the slope stability, the obtained HB envelope is employed in a multi-cone mechanism based on upper bound limit analysis (UBLA). Specifically, the counter of this mechanism is determined by 10 optimized dependent rupture angles, highly considering the nonlinearity of the Hoek-Brown envelope compared to the traditional linearization strategies. As an improved seismic stability analysis, the seismic load is characterized by a modified pseudo-dynamic method. Analytical expressions for two stability indicators, i.e., the stability number and safety factor, are derived. Comparisons with numerical simulations demonstrate the rationality of the proposed procedure. Parametric analysis indicates the significant effect of the tension cut-off on the HB rock slope stability and the contour of the sliding surface.

A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

12328 Multimodal Imaging Reveals Changes In Beta Cell Metabolism And Heterogeneity Throughout Pregnancy

Abstract Disclosure: J.M. Cuala: None. T. Singh: None. C. Deng: None. K. Jayapalan: None. S. Dhawan: None. K. White: None. S. Georgia: None. During pregnancy, a unique, transient metabolic stressor, pancreatic islets adapt by balancing hormone production to maintain tight glucose homeostasis. Maternal insulin demand increases as systemic insulin resistance increases. To keep up with the increase in demand, beta cell functional dynamics change throughout pregnancy, including increasing insulin synthesis and lowering the threshold for glucose-stimulated insulin secretion (GSIS). Failure to adapt can lead to gestational diabetes (GD), a complication in 14% of pregnancies worldwide, feeding into a vicious cycle of impaired function and, eventually, hyperglycemia. Fundamentally, beta cell function is dictated by its metabolism, calcium signaling, and insulin content upon glucose stimulation. To better understand how beta cell maladaptation during pregnancy leads to gestational diabetes, we propose to apply a multidisciplinary approach to integrate novel tools to assess function and metabolism fluctuations throughout normal pregnancy. Using live isolated islets from wild-type C57BL/6 (BL6) and InsCre-gCAMP6f fl/fl (INS-gCAMP) mice, we will measure GSIS and take 4D images using metabolic fluorescence lifetime imaging (FLIM) and confocal microscopy to visualize and quantify metabolic and functional changes at different time points at cellular resolution. We expect functional and metabolic capacity levels to increase during the onset of beta cell remodeling (∼Day 8) and continue to peak throughout pregnancy. In contrast, changes in beta cell heterogeneity become more evident at the peaks of beta cell proliferation and expansion (∼Day 11-14). The proposed study will implement novel techniques to establish beta cell functional and metabolic information within a 3D pancreatic islet context throughout different time points of pregnancy. With this, we will provide greater insight into beta cell biology and its adaptation to metabolic stress to better understand the progression of gestational diabetes, which can then be applied to investigate other cellular adaptive dynamics that affect health and disease. Presentation: 6/2/2024

Depth-Aware Networks for Multi-Organ Lesion Detection in Chest CT Scans.

Computer tomography (CT) scans' capabilities in detecting lesions have been increasing remarkably in the past decades. In this paper, we propose a multi-organ lesion detection (MOLD) approach to better address real-life chest-related clinical needs. MOLD is a challenging task, especially within a large, high resolution image volume, due to various types of background information interference and large differences in lesion sizes. Furthermore, the appearance similarity between lesions and other normal tissues demands more discriminative features. In order to overcome these challenges, we introduce depth-aware (DA) and skipped-layer hierarchical training (SHT) mechanisms with the novel Dense 3D context enhanced (Dense 3DCE) lesion detection model. The novel Dense 3DCE framework considers the shallow, medium, and deep-level features together comprehensively. In addition, equipped with our SHT scheme, the backpropagation process can now be supervised under precise control, while the DA scheme can effectively incorporate depth domain knowledge into the scheme. Extensive experiments have been carried out on a publicly available, widely used DeepLesion dataset, and the results prove the effectiveness of our DA-SHT Dense 3DCE network in the MOLD task.

RETRACTED ARTICLEResearch Progress on 2D Human Pose Estimation Based on Deep Learning

Human pose estimation (HPE) is a significant area of study within computer vision and artificial intelligence, which locates the position and pose of human joints through images or videos. HPE has advanced progress in computer vision and deep learning fields at the theoretical level, improved the efficiency and accuracy of neural networks, and supported the development of natural and efficient human-computer interaction systems. At the application level, HPE has extensive value in many fields, including security monitoring, motion analysis and health management, VR&AR, robotics technology, film and television production and animation, intelligent driving and assisted driving. This paper divides 2D HPE tasks into three categories: two-stage (top-down and bottom-up), single-stage, and other improvement strategies (Transformer-based methods and post-processing optimization methods). Although deep learning-based pose estimation algorithms have achieved remarkable results, they still face challenges, such as prediction in complex scenes, crowd and joint occlusion problems, and lightweight design of models. Future work should pay more attention to these challenges and extend pose estimation technology from 2D to 3D contexts.

Multimodal mechano-microscopy reveals mechanical phenotypes of breast cancer spheroids in three dimensions.

Cancer cell invasion relies on an equilibrium between cell deformability and the biophysical constraints imposed by the extracellular matrix (ECM). However, there is little consensus on the nature of the local biomechanical alterations in cancer cell dissemination in the context of three-dimensional (3D) tumor microenvironments (TMEs). While the shortcomings of two-dimensional (2D) models in replicating in situ cell behavior are well known, 3D TME models remain underutilized because contemporary mechanical quantification tools are limited to surface measurements. Here, we overcome this major challenge by quantifying local mechanics of cancer cell spheroids in 3D TMEs. We achieve this using multimodal mechano-microscopy, integrating optical coherence microscopy-based elasticity imaging with confocal fluorescence microscopy. We observe that non-metastatic cancer spheroids show no invasion while showing increased peripheral cell elasticity in both stiff and soft environments. Metastatic cancer spheroids, however, show ECM-mediated softening in a stiff microenvironment and, in a soft environment, initiate cell invasion with peripheral softening associated with early metastatic dissemination. This exemplar of live-cell 3D mechanotyping supports that invasion increases cell deformability in a 3D context, illustrating the power of multimodal mechano-microscopy for quantitative mechanobiology in situ.

Evaluating and Modeling the Effect of Frame Rate on Steering Performance in Virtual Reality.

Prior work has shown that frame rate significantly influences user behavior in fast-response tasks in 2D and 3D contexts. However, its impact on a steering task, which involves navigating an object along a path from the start to the end, remains relatively unexplored, especially in the context of virtual reality (VR). This task is considered a typical non-fast-response activity, as it does not demand rapid reactions within a limited time frame. Our work aims to understand and model users' steering behavior and predict movement time with different task complexities and frame rates in VR environments. We first conducted a user study to collect user behavior in a steering task with four factors: frame rate, path length, width, and radius of curvature. Based on the results, we then quantified the effects of frame rate and built two predictive models. Our models exhibited the best fit ([Formula: see text]) and over 17% improvement in prediction accuracy for movement time compared to existing models. Our models' robustness was further validated by applying them to predict steering performance with different VR tasks and frame rates. The two models keep the best predictability for both movement time and speed.

Engineered 3D Periodontal Ligament Model with Magnetic Tensile Loading

In vitro models are invaluable tools for deconstructing the biological complexity of the periodontal ligament (PDL). Model systems that closely reproduce the 3-dimensional (3D) configuration of cell–cell and cell–matrix interactions in native tissue can deliver physiologically relevant insights. However, 3D models of the PDL that incorporate mechanical loading are currently lacking. Hence, we developed a model where periodontal tissue constructs (PTCs) are made by casting PDL cells in a collagen gel suspended between a pair of slender, silicone posts for magnetic tensile loading. Specifically, one of the posts was rigid and the other was flexible with a magnet embedded in its tip so that PTCs could be subjected to tensile loading with an external magnet. Additionally, the deflection of the flexible post could be used to measure the contractile force of PDL cells in the PTCs. Prior to tensile loading, second harmonics generation analysis of collagen fibers in PTCs revealed that incorporation of PDL cells resulted in collagen remodeling. Biomechanical testing of PTCs by tensile loading revealed an elastic response at 4 h, permanent deformation by 1 d, and creep elongation by 1 wk. Subsequently, contractile forces of PDL cells were substantially lower for PTCs under tensile loading. Immunofluorescence analysis revealed that tensile loading caused PDL cells to increase in number, express higher levels of F-actin and α–smooth muscle actin, and become aligned to the tensile axis. Second harmonics generation analysis indicated that collagen fibers in PTCs progressively remodeled over time with tensile loading. Gene expression analysis also confirmed tension-mediated upregulation of the F-actin/Rho pathway and osteogenic genes. Our model is novel in demonstrating the mechanobiological behavior that results in cell-mediated remodeling of the PDL tissue in a 3D context. Hence, it can be a valuable tool to develop therapeutics for periodontitis, periodontal regeneration, and orthodontics.

Direct visualization of emergent metastatic features within an ex vivo model of the tumor microenvironment.

Metabolic conditions such as hypoxia, nutrient starvation, and media acidification, together with interactions with stromal cells are critical drivers of metastasis. Since these conditions arise deep within tumor tissues with poor access to the bloodstream, the observation of nascent metastases in vivo is exceedingly challenging. On the other hand, conventional cell culture studies cannot capture the complex nature of metastatic processes. We thus designed and implemented an ex vivo model of the tumor microenvironment to study the emergence of metastatic features in tumor cells in their native 3-dimensional (3D) context. In this system, named 3MIC, tumor cells spontaneously create ischemic-like conditions, and it allows the direct visualization of tumor-stroma interactions with high spatial and temporal resolution. We studied how 3D tumor spheroids evolve in the 3MIC when cultured under different metabolic environments and in the presence or absence of stromal cells. Consistent with previous experimental and clinical data, we show that ischemic environments increase cell migration and invasion. Importantly, the 3MIC allowed us to directly observe the emergence of these pro-metastatic features with single-cell resolution allowing us to track how changes in tumor motility were modulated by macrophages and endothelial cells. With these tools, we determined that the acidification of the extracellular media was more important than hypoxia in the induction of pro-metastatic tumor features. We also illustrate how the 3MIC can be used to test the effects of anti-metastatic drugs on cells experiencing different metabolic conditions. Overall, the 3MIC allows us to directly observe the emergence of metastatic tumor features in a physiologically relevant model of the tumor microenvironment. This simple and cost-effective system can dissect the complexity of the tumor microenvironment to test perturbations that may prevent tumors from becoming metastatic.

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids.

Multicellular tumor spheroids are a popular 3D tissue microaggregatemodel for reproducing tumor microenvironment, testing and optimizing drug therapiesand using bio- and nanosensors in a 3D context. Their ease of production, predictable size, growth, and observed nutrient and metabolite gradients are important to recapitulate the 3D niche-likecell microenvironment. However, spheroid heterogeneity and variability of their production methods can influence overall cell metabolism, viability, and drug response. This makes it difficult to choose the most appropriatemethodology, considering the requirements in size, variability, needs of biofabrication, and use as in vitro 3D tissue models in stem and cancer cell biology. In particular, spheroid production can influence their compatibility with quantitative live microscopies, such as optical metabolic imaging, fluorescence lifetime imaging microscopy (FLIM), monitoring of spheroidhypoxia with nanosensors, or viability. Here, a number of conventional spheroid formation protocols are presented, highlighting their compatibility with the live widefield, confocal, and two-photon microscopies. The follow-up imaging to analysispipeline with multiplexed autofluorescence FLIM and, using various types of cancer and stem cell spheroids, is also presented.

Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism

Skin microvasculature is vital for human cardiovascular health and thermoregulation, but its imaging and analysis presents significant challenges. Statistical methods such as speckle decorrelation in optical coherence tomography angiography (OCTA) often require multiple co-located B-scans, leading to lengthy acquisitions prone to motion artefacts. Deep learning has shown promise in enhancing accuracy and reducing measurement time by leveraging local information. However, both statistical and deep learning methods typically focus solely on processing individual 2D B-scans, neglecting contextual information from neighbouring B-scans. This limitation compromises spatial context and disregards the 3D features within tissue, potentially affecting OCTA image accuracy. In this study, we propose a novel approach utilising 3D convolutional neural networks (CNNs) to address this limitation. By considering the 3D spatial context, these 3D CNNs mitigate information loss, preserving fine details and boundaries in OCTA images. Our method reduces the required number of B-scans while enhancing accuracy, thereby increasing clinical applicability. This advancement holds promise for improving clinical practices and understanding skin microvascular dynamics crucial for cardiovascular health and thermoregulation.

Explicit Incorporation of Spatial Autocorrelation in 3D Deep Learning for Geospatial Object Detection

Three-dimensional (3D) geospatial object detection has become essential for 3D geospatial studies driven by explosive growth in 3D data. It is extremely labor- and cost-intensive, though, as it often requires manual detection. Deep learning has been recently used to automate object detection within 3D context. Yet, addressing spatial dependency in 3D data and how it might inform deep learning for 3D geospatial object detection remains a significant challenge. Traditional models focus on the use of spatial properties, often overlooking color and contextual information. Exploiting these nonspatial attributes for 3D geospatial object detection thus becomes crucial. Our study pioneers explicit incorporation of spatial autocorrelation of color information into 3D deep learning for object detection. We introduce an innovative framework to estimate spatial autocorrelation, addressing challenges in unstructured 3D data sets. Our experiments suggest the effectiveness of incorporating spatial autocorrelation features in enhancing the accuracy of 3D deep learning models for geospatial object detection. We further investigate the uncertainty of such contextual information brought by diverse configurations, exemplified by the number of nearest neighbors. This study advances 3D geospatial object detection via using spatial autocorrelation to inform deep learning algorithms, strengthening the connection between GIScience and artificial intelligence and, thus, holding implications for diverse GeoAI applications.

A NSC-34 cell line-derived spheroid model: Potential and challenges for in vitro evaluation of neurodegeneration.

Three-dimensional (3D) spheroid models aim to bridge the gap between traditional two-dimensional (2D) cultures and the complex in vivo tissue environment. These models, created by self-clustering cells to mimic a 3D environment with surrounding extracellular framework, provide a valuable research tool. The NSC-34 cell line, generated by fusing mouse spinal cord motor neurons and neuroblastoma cells, is essential for studying neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), where abnormal protein accumulation, such as TAR-DNA-binding protein 43 (TDP-43), occurs in affected nerve cells. However, NSC-34 behavior in a 3D context remains underexplored, and this study represents the first attempt to create a 3D model to determine its suitability for studying pathology. We generated NSC-34 spheroids using a nonadhesive hydrogel-based template and characterized them for 6 days. Light microscopy revealed that NSC-34 cells in 3D maintained high viability, a distinct round shape, and forming stable membrane connections. Scanning electron microscopy identified multiple tunnel-like structures, while ultrastructural analysis highlighted nuclear bending and mitochondria alterations. Using inducible GFP-TDP-43-expressing NSC-34 spheroids, we explored whether 3D structure affected TDP-43 expression, localization, and aggregation. Spheroids displayed nuclear GFP-TDP-43 expression, albeit at a reduced level compared with 2D cultures and generated both TDP-35 fragments and TDP-43 aggregates. This study sheds light on the distinctive behavior of NSC-34 in 3D culture, suggesting caution in the use of the 3D model for ALS or TDP-43 pathologies. Yet, it underscores the spheroids' potential for investigating fundamental cellular mechanisms, cell adaptation in a 3D context, future bioreactor applications, and drug penetration studies. RESEARCH HIGHLIGHTS: 3D spheroid generation: NSC-34 spheroids, developed using a hydrogel-based template, showed high viability and distinct shapes for 6 days. Structural features: advanced microscopy identified tunnel-like structures and nuclear and mitochondrial changes in the spheroids. Protein dynamics: the study observed how 3D structures impact TDP-43 behavior, with altered expression but similar aggregation patterns to 2D cultures. Research implications: this study reveals the unique behavior of NSC-34 in 3D culture, suggests a careful approach to use this model for ALS or TDP-43 pathologies, and highlights its potential in cellular mechanism research and drug testing applications.

FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities

Abstract Light availability (LAv) dictates a variety of biological and ecological processes across a range of spatiotemporal scales. Quantifying the spatial pattern of LAv in three‐dimensional (3D) space can promote the understanding of microclimates that are critical to fine‐scale species distribution. However, there is still a lack of tools that are robust to evaluate spatiotemporal heterogeneity of LAv in forests. Here, we propose the Forest Light Analyzer python package (FLApy), an open‐source computational tool designed for the analysis of intra‐forest LAv variation across multiple spatial scales. FLApy is freely invoked by Python, facilitating the processing of LiDAR point cloud data into a 3D data container constructed by voxels, as well as traversal calculations related to the LAv regime by high performance synthetic hemispherical algorithm. Furthermore, FLApy incorporates 37 indicators, enabling users to expediently export and visualize LAv patterns and the evaluation of heterogeneity of LAv at two scales (voxel scale and 3D‐cluster scale) for a range of fine‐scale ecological study purposes. To validate the efficacy of the FLApy, we employed a simulated point cloud dataset that simulates forests (varying in canopy closure). Furthermore, to evaluate real world forest, we executed the standard workflow of FLApy utilizing drone‐derived data from three subtropical evergreen broad‐leaved forest dynamics plots within the Ailao Mountain Reserve. Our findings underscore that a series of indices derived from FLApy provide a robust characterization of light availability heterogeneity within diverse forest settings. Additionally, when juxtaposed with conventional monitoring techniques, the metrics offered by FLApy demonstrated better generality in our field assessments. FLApy offers ecologists a solution for rapid quantification of understory light 3D‐regimes across multiple scales, addressing the disparity between traditional manual approaches and the precision required for contemporary ecological studies. Moreover, FLApy provides robust support for the establishment and expansion of heterogeneity indices based on 3D micro‐environments, enhancing our understanding of the largely uncharted 3D structural patterns. Anticipated outcomes suggest that FLApy will enhance our knowledge concerning the intra‐forest climatic conditions into a 3D context, proving pivotal in the delineation of microhabitats and the development of detailed 3D‐scale species distribution models.

Deep 3D histology powered by tissue clearing, omics and AI.

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.