3D Context Research Articles

MACE: A Machine-learning Approach to Chemistry Emulation

The chemistry of an astrophysical environment is closely coupled to its dynamics, the latter often found to be complex. Hence, to properly model these environments a 3D context is necessary. However, solving chemical kinetics within a 3D hydro simulation is computationally infeasible for even a modest parameter study. In order to develop a feasible 3D hydro-chemical simulation, the classical chemical approach needs to be replaced by a faster alternative. We present mace, a Machine-learning Approach to Chemistry Emulation, as a proof-of-concept work on emulating chemistry in a dynamical environment. Using the context of AGB outflows, we have developed an architecture that combines the use of an autoencoder (to reduce the dimensionality of the chemical network) and a set of latent ordinary differential equations (that are solved to perform the temporal evolution of the reduced features). Training this architecture with an integrated scheme makes it possible to successfully reproduce a full chemical pathway in a dynamical environment. mace outperforms its classical analog on average by a factor of 26. Furthermore, its efficient implementation in PyTorch results in a sublinear scaling with respect to the number of hydrodynamical simulation particles.

Visualising cancer in 3D: 3-Dimensional Tissue Imaging for management of cutaneous basal cell carcinoma.

Surgical management of basal cell carcinoma (BCC) typically involves surgical excision with post-operative margin assessment using the bread-loafing technique; or gold-standard Mohs micrographic surgery (MMS), where margins are iteratively examined for residual cancer after tumour removal, with additional excisions performed upon detecting residual tumour at margins. There is limited sampling of resection margins with bread loafing, with detection of positive margins 44% of the time using 2 mm intervals. To resolve this, we have developed three-dimensional (3D) Tissue Imaging for: (1) complete examination of cancer margins and (2) detection of tumour proximity to nerves and blood vessels. 3D Tissue optical clearing with a light sheet imaging protocol was developed for margin assessment in two datasets assessed by two independent evaluators: (1) 48 samples from 29 patients with varied BCC subtypes, sizes and pigmentation levels; (2) 32 samples with matching Mohs' surgeon reading of tumour margins using two-dimensional haematoxylin & eosin-stained sections. The 3D Tissue Imaging protocol permits a complete examination of deeper and peripheral margins. Two independent evaluators achieved negative predictive values of 92.3% and 88.24% with 3D Tissue Imaging. Images obtained from 3D Tissue Imaging recapitulates histological features of BCC, such as nuclear crowding, palisading and retraction clefting and provides a 3D context for recognising normal skin adnexal structures. Concurrent immunofluorescence labelling of nerves and blood vessels allows visualisation of structures closer to tumour-positive regions, which may have a higher risk for neural and vascular infiltration. Together, this method provides more information in a 3D spatial context, enabling better cancer management by clinicians.

Integration of autonomous maximal smile 3D image with digital 3D dental model and investigation of its accuracy.

This study aims to establish an approach to integrate autonomous maximal smile (AMS) 3D facial image with digital 3D dental models to demonstrate the digital orthodontic set-up in the 3D facial context. Using Geomagic Studio software, the AMS 3D facial image and pre-treatment dental model were manually and globally registered. Subsequently, the pre-treatment dental model was substituted with the predicted post-treatment dental model. The intraoral region of the AMS 3D facial image was removed, achieving a conjunctive display of the AMS 3D facial image and the post-treatment dental set-up. The distances between four groups of corresponding landmark pairs on the AMS 3D facial image and the pre-treatment dental set-up were calculated, and the accuracy of the registration operation was evaluated by paired t-test. The novel approach effectively facilitated the integration of AMS 3D facial images with the pre-treatment and predicted post-treatment 3D dental models. The average distances between the pairs of points were (1.19±0.55) mm and (1.55±0.59) mm for the two registrations, respectively. Notably, no statistically significant difference was observed between the two measurements (P>0.05), indicating a high agreement (intraclass correlation coefficient=0.914). This study established an approach to integrate AMS 3D facial images with digital 3D dental models. Through this approach, the digital orthodontic set-up design can be displayed in the context of a 3D facial image, which may help to improve the quality of outcome set-up in digital orthodontics, such as clear aligner therapy.

View adaptive unified self-supervised technique for abdominal organ segmentation

Automatic abdominal organ segmentation is an essential prerequisite for accurate volumetric analysis, disease diagnosis, and tracking by medical practitioners. However, the deformable shapes, variable locations, overlapping with nearby organs, and similar contrast make the segmentation challenging. Moreover, the requirement of a large manually labeled dataset makes it harder. Hence, a semi-supervised contrastive learning approach is utilized to perform the automatic abdominal organ segmentation. Existing 3D deep learning models based on contrastive learning are not able to capture the 3D context of medical volumetric data along three planes/views: axial, sagittal, and coronal views. In this work, a semi-supervised view-adaptive unified model (VAU-model) is proposed to make the 3D deep learning model as view-adaptive to learn 3D context along each view in a unified manner. This method utilizes the novel optimization function that assists the 3D model to learn the 3D context of volumetric medical data along each view in a single model. The effectiveness of the proposed approach is validated on the three types of datasets: BTCV, NIH, and MSD quantitatively and qualitatively. The results demonstrate that the VAU model achieves an average Dice score of 81.61% which is a 3.89% improvement compared to the previous best results for pancreas segmentation in multi-organ dataset BTCV. It also achieves an average Dice score of 77.76% and 76.76% for the pancreas under the single organ non-pathological NIH dataset, and pathological MSD dataset.

RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration.

Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines under large rotations, or encode local geometry that is less distinctive. To address this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse local regions, rotation-invariant local geometry is encoded into geometric descriptors. Global awareness of 3D structures and geometric context is subsequently incorporated, both in a rotation-invariant fashion. More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions. Geometric context from the whole scene is then globally aggregated into descriptors. Finally, the description of sparse regions is interpolated to dense point descriptors, from which correspondences are extracted for registration. To validate our approach, we conduct extensive experiments on both object- and scene-level data. With large rotations, RIGA surpasses the state-of-the-art methods by a margin of 8 ° in terms of the Relative Rotation Error on ModelNet40 and improves the Feature Matching Recall by at least 5 percentage points on 3DLoMatch.

T staging esophageal tumors with x rays.

With histopathology results typically taking several days, the ability to stage tumors during interventions could provide a step change in various cancer interventions. X-ray technology has advanced significantly in recent years with the introduction of phase-based imaging methods. These have been adapted for use in standard labs rather than specialized facilities such as synchrotrons, and approaches that enable fast 3D scans with conventional x-ray sources have been developed. This opens the possibility to produce 3D images with enhanced soft tissue contrast at a level of detail comparable to histopathology, in times sufficiently short to be compatible with use during surgical interventions. In this paper we discuss the application of one such approach to human esophagi obtained from esophagectomy interventions. We demonstrate that the image quality is sufficiently high to enable tumor staging based on the x-ray datasets alone. Alongside detection of involved margins with potentially life-saving implications, staging tumors intra-operatively has the potential to change patient pathways, facilitating optimization of therapeutic interventions during the procedure itself. Besides a prospective intra-operative use, the availability of high-quality 3D images of entire esophageal tumors can support histopathological characterization, from enabling "right slice first time" approaches to understanding the histopathology in the full 3D context of the surrounding tumor environment.

Illuminating the complete ß-cell mass of the human pancreas- signifying a new view on the islets of Langerhans

Pancreatic islets of Langerhans play a pivotal role in regulating blood glucose homeostasis, but critical information regarding their mass, distribution and composition is lacking within a whole organ context. Here, we apply a 3D imaging pipeline to generate a complete account of the insulin-producing islets throughout the human pancreas at a microscopic resolution and within a maintained spatial 3D context. These data show that human islets are far more heterogenous than previously accounted for with regards to their size distribution and cellular make up. By deep tissue 3D imaging, this in-depth study demonstrates that 50% of the human insulin-expressing islets are virtually devoid of glucagon-producing α-cells, an observation with significant implications for both experimental and clinical research.

Survey of Connectivity Restoration in 3D Wireless Ad Hoc/Sensor Networks

Wireless ad hoc/sensor networks (WASN) have seen increased application in three-dimensional (3D) environments, such as underwater and aerial scenarios. However, WASN may be fragmented or unable to connect continuously due to the harsh surrounding environment or high mobility. Therefore, restoring the network connectivity and transmit data in real time is very important. This paper focuses on the critical task of restoring network connectivity in 3D WASN, a complex issue given that existing connectivity restoration algorithms for two-dimensional environments are not directly applicable or become overly complicated in 3D contexts. We present a comprehensive analysis of the current research landscape, summarizing key findings related to various aspects of 3D WASN connectivity restoration. These aspects include the application environment (underwater, in the sky, and recovery disaster), opportunities for restoration (active, passive, and active/passive), implementation strategies (clustering and sleep scheduling), and resource constraints (node deployment and movement control). Our study also proposes a classification of connectivity restoration solutions for 3D WASN, identifying existing gaps and suggesting potential future research directions. By providing specific insights and a structured overview of the field, we aim to contribute to the ongoing development of robust and resilient 3D WASN.

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation.

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i.e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

The influence of depth on object selection and manipulation in visual working memory within a 3D context.

Recent studies have examined whether the internal selection mechanism functions similarly for perception and visual working memory (VWM). However, the process of how we access and manipulate object representations distributed in a 3D space remains unclear. In this study, we utilized a memory search task to investigate the effect of depth on object selection and manipulation within VWM. The memory display consisted of colored items half positioned at the near depth plane and the other half at the far plane. During memory maintenance, the participants were instructed to search for a target representation and update its color. The results showed that under object-based attention (Experiments 1, 3, and 5), the update time was faster for targets at the near plane than for those at the far plane. This effect was absent in VWM when deploying spatial attention (Experiment 2) and in visual search regardless of the type of attention deployed (Experiment 4). The differential effects of depth on spatial and object-based attention in VWM suggest that spatial attention primarily relied on 2D location information irrespective of depth, whereas object-based attention seemed to prioritize memory representations at the front plane before shifting to the back. Our findings shed light on the interaction between depth perception and the selection mechanisms within VWM in a 3D context, emphasizing the importance of ordinal, rather than metric, spatial information in guiding object-based attention in VWM.

Abstract 5658: Genome-scale CRISPR screen in 3D tumor models identifies EGFR-TKI resistance mechanisms in human NSCLC

Abstract INTRODUCTION: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are the primary treatment for EGFR-mutated non-small-cell lung cancer (NSCLC). Despite Osimertinib, an FDA-approved EGFR-TKI, showing high selectivity for EGFR-activating mutations, resistance inevitably arises, leading to disease progression. The influence of genes within cancer on Osimertinib response remains unclear. To tackle this challenge, CRISPR screens have been extensively used. However, current CRISPR screens investigating EGFR TKI resistance heavily rely on 2D monolayer cell models, lacking the ability to replicate in vivo tumors accurately. In response, we conducted a genome-scale loss-of-function CRISPR screening employing 3D tumor models embedded in an extracellular matrix across various NSCLC cell lines. Utilizing a comprehensive panel of CRISPR screens in both 3D tumor models and 2D monolayer models, we systematically assessed how genetic alterations impact cell resistance and transformation against treatment with Osimertinib, in diverse cellular contexts. METHOD: Genome-scale CRISPR-Cas9 screens in 10 NSCLC cell lines used 3D tumor models and 2D cells to pinpoint genes pivotal for EGFR TKI resistance. Cas9-expressing cell lines were transduced with a lentivirus carrying 60,000 sgRNAs targeting ~20,000 human genes. Cells were cultured in both models and treated with optimized Osimertinib or DMSO, inducing 20-30% cell growth inhibition for 3 days. Post-treatment, cells were recovered for 4 days. This treatment cycle was repeated 3-4 times in 3D models and 2-3 times in 2D cells. The sgRNA composition in each pool was analyzed using Next Generation Sequencing after treatment cycles in both models. RESULTS: Upon analyzing CRISPR screening data across 10 distinct cell lines to assess drug phenotypes, we confirmed the enrichment of established resistance pathways like PI3K/AKT/mTOR, Hippo, and MAPK signaling pathways in both 2D monolayer cells and 3D tumor models. However, notable discrepancies in EGFR TKI resistance mechanisms were evident between these two models. Specifically, CRISPR screens conducted with 3D tumor models unveiled unique resistance mechanisms, notably the LKB1/AMPK/mTOR pathway (STK11, GSK3B), Ubiquitination (RNF7, ARIH2), and stabilization of E-cadherin adherens junction (NCKAP1, EXOC4) differing from those identified in conventional 2D monolayer model. Furthermore, the 3D tumor models exhibited significantly heightened enrichment of the MAPK and Hippo signaling pathways compared to their drug phenotype score in 2D monolayer cells. CONCLUSION: This study underscores the significance of employing the 3D tumor model in drug phenotype CRISPR screen. This approach recapitulates unique gene dependency patterns and interactions linked to EGFR-TKI resistance in a 3D tumor context, revealing potential targetable vulnerabilities context-dependent. Citation Format: Pranay Agarwal, Soon Youn Choi, Xin Liu, Jina Song, Maya Tureez, Aashka Mehta, Un Jae Baek, Kyuho Han, Hong-Pyo Lee. Genome-scale CRISPR screen in 3D tumor models identifies EGFR-TKI resistance mechanisms in human NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5658.

In vitro and in ovo photodynamic efficacy of nebulized curcumin-loaded tetraether lipid liposomes prepared by DC as stable drug delivery system

Lung cancer is one of the most common causes of high mortality worldwide. Current treatment strategies, e.g., surgery, radiotherapy, chemotherapy, and immunotherapy, insufficiently affect the overall outcome. In this study, we used curcumin as a natural photosensitizer in photodynamic therapy and encapsulated it in liposomes consisting of stabilizing tetraether lipids aiming for a pulmonary drug delivery system against lung cancer. The liposomes with either hydrolyzed glycerol-dialkyl-glycerol tetraether (hGDGT) in different ratios or hydrolyzed glycerol-dialkyl-nonitol tetraether (hGDNT) were prepared by dual centrifugation (DC), an innovative method for liposome preparation. The liposomes’ physicochemical characteristics before and after nebulization and other nebulization characteristics confirmed their suitability. Morphological characterization using atomic force and transmission electron microscopy showed proper vesicular structures indicative of liposomes. Qualitative and quantitative uptake of the curcumin-loaded liposomes in lung adenocarcinoma (A549) cells was visualized and proven. Phototoxic effects of the liposomes were detected on A549 cells, showing decreased cell viability. The generation of reactive oxygen species required for PDT and disruption of mitochondrial membrane potential were confirmed. Moreover, the chorioallantoic membrane (CAM) model was used to further evaluate biocompatibility and photodynamic efficacy in a 3D cell culture context. Photodynamic efficacy was assessed by PET/CT after nebulization of the liposomes onto the xenografted tumors on the CAM with subsequent irradiation. The physicochemical properties and the efficacy of tetraether lipid liposomes encapsulating curcumin, especially liposomes containing hGDNT, in 2D and 3D cell cultures seem promising for future PDT usage against lung cancer.

Molecular mapping of neuronal architecture using STORM microscopy and new fluorescent probes for SMLM imaging.

Imaging neuronal architecture has been a recurrent challenge over the years, and the localization of synaptic proteins is a frequent challenge in neuroscience. To quantitatively detect and analyze the structure of synapses, we recently developed free SODA software to detect the association of pre and postsynaptic proteins. To fully take advantage of spatial distribution analysis in complex cells, such as neurons, we also selected some new dyes for plasma membrane labeling. Using Icy SODA plugin, we could detect and analyze synaptic association in both conventional and single molecule localization microscopy, giving access to a molecular map at the nanoscale level. To replace those molecular distributions within the neuronal three-dimensional (3D) shape, we used MemBright probes and 3D STORM analysis to decipher the entire 3D shape of various dendritic spine types at the single-molecule resolution level. We report here the example of synaptic proteins within neuronal mask, but these tools have a broader spectrum of interest since they can be used whatever the proteins or the cellular type. Altogether with SODA plugin, MemBright probes thus provide the perfect toolkit to decipher a nanometric molecular map of proteins within a 3D cellular context.

Assessing the advantages of 3D bioprinting and 3D spheroids in deciphering the osteoarthritis healing mechanism using human chondrocytes and polarized macrophages

The molecular niche of an osteoarthritic microenvironment comprises the native chondrocytes, the circulatory immune cells, and their respective inflammatory mediators. Although M2 macrophages infiltrate the joint tissue during osteoarthritis (OA) to initiate cartilage repair, the mechanistic crosstalk that dwells underneath is still unknown. Our study established a co-culture system of human OA chondrocytes and M2 macrophages in 3D spheroids and 3D bioprinted silk-gelatin constructs. It is already well established that Silk fibroin-gelatin bioink supports chondrogenic differentiation due to upregulation of the Wnt/β-catenin pathway. Additionally, the presence of anti-inflammatory M2 macrophages significantly upregulated the expression of chondrogenic biomarkers (COL-II, ACAN) with an attenuated expression of the chondrocyte hypertrophy (COL-X), chondrocyte dedifferentiation (COL-I) and matrix catabolism (MMP-1 and MMP-13) genes even in the absence of the interleukins. Furthermore, the 3D bioprinted co-culture model displayed an upper hand in stimulating cartilage regeneration and OA inhibition than the spheroid model, underlining the role of silk fibroin-gelatin in encouraging chondrogenesis. Additionally, the 3D bioprinted silk-gelatin constructs further supported the maintenance of stable anti-inflammatory phenotype of M2 macrophage. Thus, the direct interaction between the primary OAC and M2 macrophages in the 3D context, along with the release of the soluble anti-inflammatory factors by the M2 cells, significantly contributed to a better understanding of the molecular mechanisms responsible for immune cell-mediated OA healing.

Imaging actin organisation and dynamics in 3D.

The actin cytoskeleton plays a critical role in cell architecture and the control of fundamental processes including cell division, migration and survival. The dynamics and organisation of F-actin have been widely studied in a breadth of cell types on classical two-dimensional (2D) surfaces. Recent advances in optical microscopy have enabled interrogation of these cytoskeletal networks in cells within three-dimensional (3D) scaffolds, tissues and in vivo. Emerging studies indicate that the dimensionality experienced by cells has a profound impact on the structure and function of the cytoskeleton, with cells in 3D environments exhibiting cytoskeletal arrangements that differ to cells in 2D environments. However, the addition of a third (and fourth, with time) dimension leads to challenges in sample preparation, imaging and analysis, necessitating additional considerations to achieve the required signal-to-noise ratio and spatial and temporal resolution. Here, we summarise the current tools for imaging actin in a 3D context and highlight examples of the importance of this in understanding cytoskeletal biology and the challenges and opportunities in this domain.

Characterization of transient and progressive pulmonary fibrosis by spatially correlated phase contrast microCT, classical histopathology and atomic force microscopy

Pulmonary fibrosis (PF) is a severe and progressive condition in which the lung becomes scarred over time resulting in pulmonary function impairment. Classical histopathology remains an important tool for micro-structural tissue assessment in the diagnosis of PF. A novel workflow based on spatial correlated propagation-based phase-contrast micro computed tomography (PBI-microCT), atomic force microscopy (AFM) and histopathology was developed and applied to two different preclinical mouse models of PF - the commonly used and well characterized Bleomycin-induced PF and a novel mouse model for progressive PF caused by conditional Nedd4-2 KO. The aim was to integrate structural and mechanical features from hallmarks of fibrotic lung tissue remodeling. PBI-microCT was used to assess structural alteration in whole fixed and paraffin embedded lungs, allowing for identification of fibrotic foci within the 3D context of the entire organ and facilitating targeted microtome sectioning of planes of interest for subsequent histopathology. Subsequently, these sections of interest were subjected to AFM to assess changes in the local tissue stiffness of previously identified structures of interest. 3D whole organ analysis showed clear morphological differences in 3D tissue porosity between transient and progressive PF and control lungs. By integrating the results obtained from targeted AFM analysis, it was possible to discriminate between the Bleomycin model and the novel conditional Nedd4-2 KO model using agglomerative cluster analysis. As our workflow for 3D spatial correlation of PBI, targeted histopathology and subsequent AFM is tailored around the standard procedure of formalin-fixed paraffin-embedded (FFPE) tissue specimens, it may be a powerful tool for the comprehensive tissue assessment beyond the scope of PF and preclinical research.

CI3D: Context Interaction for Dynamic Objects and Static Map Elements in 3D Driving Scenes.

Multi-view 3D visual perception including 3D object detection and Birds'-eye-view (BEV) map segmentation is essential for autonomous driving. However, there has been little discussion about 3D context attention between dynamic objects and static elements with multi-view camera inputs, due to the challenging nature of recovering the 3D spatial information from images and performing effective 3D context interaction. 3D context information is expected to provide more cues to enhance 3D visual perception for autonomous driving. We thus propose a new transformer-based framework named CI3D in an attempt to implicitly model 3D context interaction between dynamic objects and static map elements. To achieve this, we use dynamic object queries and static map queries to gather information from multi-view image features, which are represented sparsely in 3D space. Moreover, a dynamic 3D position encoder is utilized to precisely generate queries' positional embeddings. With accurate positional embeddings, the queries effectively aggregate 3D context information via a multi-head attention mechanism to model 3D context interaction. We further reveal that sparse supervision signals from the limited number of queries result in the issue of rough and vague image features. To overcome this challenge, we introduce a panoptic segmentation head as an auxiliary task and a 3D-to-2D deformable cross-attention module, greatly enhancing the robustness of spatial feature learning and sampling. Our approach has been extensively evaluated on two large-scale datasets, nuScenes and Waymo, and significantly outperforms the baseline method on both benchmarks.

Building Biomaterials to Mimic 3D Cell-Cell Junctions.

Cell-cell interactions typically occur in a 3D context that is distinct from conventional 2D cell-substrate interactions in a Petri dish. Here, we describe a benchtop method to combine a 2D extracellular matrix surface with a 3D, vertical boundary functionalized with the extracellular domain of E-cadherin. The methodology is suitable for any biology laboratory without requiring advanced microfabrication equipment or training. Overall, this cell-mimetic interface uniquely recapitulates key aspects of cell-cell adhesion and can serve as a versatile, reductionist technique to study general cell-cell interactions in a 3D context.

Learning Analytics and Knowledge Management Systems for Higher Education

The proposed research investigates the impact of communication technologies and information systems on knowledge management in e-learning environments. The authors leverage the established models of Nonaka & Takeuchi and Harsh (including knowledge reusability and sharing in a 3D context) to explore this relationship. Their findings suggest that incorporating knowledge sharing and reusability significantly enhances e-learning effectiveness. Furthermore, research indicates that data analytics (DA) presents a valuable tool for enriching the learning experience. DA facilitates the discovery of new learning areas and fosters qualitative learning within higher education. This is achieved through its extensive capabilities in analyzing existing knowledge. To ensure the inclusion of quality knowledge, the well-regarded Approach- Deploy-Review-Improve (ADRI) model is also considered. Finally, the potential of knowledge repositories in diverse educational settings is emphasized.

Design Suggestions for Digital Workflow Oriented Desktop VR Spaces

This paper presents an examination of design prin- ciples for 3D Virtual Reality (VR) environments, with a focus on enhancing digital workflows. Employing objective data, the study sets out to clarify the primary design considerations for crafting effective 3D VR spaces. Through empirical research, the authors conducted comparative analyses of task performance within both classical 2D Windows and in 3D VR environmental contexts, exploring users’ perceived difficulty levels alongside eye-tracking data. The findings reveal that, although 3D VR environments rich in distracting elements and demanding high navigational effort increase perceived task difficulty, these factors do not negatively impact overall performance or task completion time. Interestingly, eye-fixation duration results indicate that visual fixation in 3D VR falls within expected norms, whereas in 2D scenarios, fixation rates are significantly higher, more than doubling those observed in 3D settings. Drawing on these insights, the paper supports the design of 3D VR spaces that are simpler and intuitive, necessitating minimal navigation, thereby optimizing task performance efficiency.