Gotowa bibliografia na temat „Visualisation abstraite 2D”

Macrophage-specific apolipoprotein E (apoE) secretion plays an important role in atherosclerosis. Recently, we established that inhibition of protein kinase A, phospholipase C, protein phosphatase 2B and calcium results in a reduction in apoE secretion. Research suggests PKC may have a role in this process, but the isoforms and pathways involved in human macrophages are unknown. Here, we investigate a role for PKC in regulating apoE secretion from primary human monocyte-derived macrophages (HMDM). HMDM treated with pan-PKC inhibitors (Calphostin C, Ro-318220, Bisindolylmaleimide I) that primarily target the classical and novel PKC isoforms result in a dose-dependent decrease in apoE secretion. Further, the specific classical PKC inhibitor, Gö6976, also resulted in a reduction in apoE secretion. Metabolic labelling studies indicate that CalpC and Gö6976 have no effect on apoE synthesis or degradation but directly inhibit the secretion of apoE. This occurred post-golgi as evidenced by normal glycosylation of apoE on 2D electrophoresis. Direct visualisation of apoE-GFP containing vesicles in HMDM by live cell confocal microscopy demonstrated a marked reduction in vesicular speed from 0.42μm/s in control cells to 0.14μm/s or 0.15μm/s by CalpC and Ro-318220 respectively. Finally, siRNA knockdown (∼70%) of the classical PKC isoforms, PKCalpha and PKCbeta, resulted in decreased basal apoE secretion (∼50%), whereas knockdown of PKCdelta (novel PKC isoform) did not. Interestingly, preliminary data demonstrate that treatment of HMDM with the PKCtheta or PKCzeta pseudosubstrate inhibitor increases apoE secretion from HMDM, whereas both the PKCepsilon translocation inhibitory peptide and siRNA knockdown of PKCepsilon did not affect apoE secretion. This suggests that apoE secretion is differentially regulated by different and specific PKC isoforms. Inhibition of PKCalpha and/or PKCbeta decreases apoE secretion by targeting the apoE trafficking pathway in a post-golgi vesicular compartment, whereas inhibition of PKCzeta (atypical PKC isoform) or PKCtheta (novel PKC isoform) increases apoE secretion. Future studies will further confirm and investigate the role of these novel and atypical PKC isoforms in regulating apoE secretion.

Avec le développement des technologies d’infographie, les données spatiales peuvent être mieux visualisées dans leur environnement 3D afin que les spectateurs puissent observer clairement les formes et les positions 3D. Parallèlement, les visualisations abstraites en 2D peuvent présenter des informations résumées, visualiser des données supplémentaires et contrôler la vue 3D. La combinaison de ces deux représentations en une seule interface peut aider les utilisateurs à entreprendre des tâches complexes, en particulier dans les domaines scientifiques, bien qu’il y ait un manque de directives générales de conception pour l’interaction. En général, les experts doivent analyser de volumineuses données scientifiques pour mener à bien des tâches difficiles. Par exemple, dans le domaine biologique, les biologistes doivent construire l’arbre de lignage cellulaire d’un embryon contenant plus de 200 cellules. Dans ce cas, le travail manuel peut être long et fastidieux, et les algorithmes d’apprentissage automatique ont le potentiel d’alléger certains des processus manuels fastidieux en fournissant des annotations ou prédictions initiales aux experts. Dans le cas du lignage cellulaire, ces prédictions contiennent toutefois des informations hiérarchiques et multicouches, et il est essentiel de les visualiser de manière séquentielle ou progressive. De plus, les représentations 3D et 2D, ainsi que les prédictions d’apprentissage automatique, doivent être connectées visuellement et interactivement dans le système.Dans cette thèse, le problème du lignage cellulaire des embryons de plantes a été le leitmotiv pour concevoir et étudier un système de visualisation qui utilise des combinaisons de représentations 3D et 2D ainsi que des visualisations pour l’apprentissage automatique. Nous avons d’abord étudié les techniques d’interaction pour la sélection 3D au sein d’un embryon de plante. Les cellules d’un embryon de plante sont jointives et constituent un ensemble d’objets 3D dense dans toutes les dimensions spatiales. Nous avons mené une étude pour évaluer trois techniques de sélection différentes, et nous avons montré que la combinaison de la technique de Sélection par Explosion et de la technique de Sélection par Liste fonctionne bien pour désigner et observer les cellules d’un embryon. Ces techniques peuvent égale- ment être étendues à d’autres données 3D denses et similaires. Deuxièmement, nous avons conçu un système de visualisations et d’interaction combiné afin que les biologistes puissent examiner les cellules de l’embryon et enregistrer l’histoire du développement dans l’arbre de lignage hiérarchique. Nous prenons en charge la construction de la hiérarchie dans deux directions, à la fois en construisant l’historique de haut en bas de l’arbre en utilisant la sélection lasso dans l’environnement 3D et de bas en haut selon le flux de travail traditionnel pour construire un arbre de lignage cellulaire hiérarchique. Nous avons également ajouté un modèle de réseau neuronal pour fournir aux biologistes des prédictions initiales sur les filiations. Nous avons réalisé une évaluation avec des biologistes ; celle- ci a montré que les représentations 3D et 2D facilitent les prises de décisions et que l’outil peut enrichir leur vision des embryons. Cependant, la performance du modèle d’apprentissage automatique n’était pas idéale. Aussi, pour faciliter le processus et améliorer les performances du modèle, dans une version plus aboutie de notre système, nous avons entraîné cinq modèles de classification différents, visualisé les prédictions et leurs incertitudes associées. Nous avons réalisé une évaluation auprès des utilisateurs ; les résultats ont indiqué que les représentations des classifieurs que nous avons conçues sont faciles à comprendre, et que le nouvel outil peut améliorer significativement les prises de décision pour la validation du lignage cellulaire
As computer graphics technologies develop, spatial data can be better visualized in the 3D environment so that viewers can observe 3D shapes and positions clearly. Meanwhile, 2D abs- tract visualizations can present summarized information, visualize additional data, and control the 3D view. Combining these two parts in one interface can assist people in finishing complicated tasks, especially in scientific domains, though there is a lack of design guidelines for the interaction. Generally, experts need to analyze large scientific data to finish challenging tasks. For example, in the biological field, biologists need to build the hierarchy tree for an embryo with more than 200 cells. In this case, manual work can be time- consuming and tedious, and machine learning algorithms have the potential to alleviate some of the tedious manual processes to serve as the basis for experts. These predictions, however, contain hierarchical and multi-layer information, and it is essential to visualize them sequentially and progressively so that experts can control their viewing pace and validation. Also, 3D and 2D representations, together with machine learning predictions, need to be visually and interactively connected in the system.In this thesis, we worked on the cell lineage problem for plant embryos as an example to investigate a visualization system and its interaction design that makes use of combinations of 3D and 2D representations as well as visualizations for ma- chine learning. We first investigated the 3D selection interaction techniques for the plant embryo.The cells in a plant embryo are tightly packed together, without any space in between. Traditionaltechniques can hardly deal with such an occlusion problem. We conducted a study to evaluate three different selection techniques, and found out that the combination of the Explosion Selection technique and the List Selection technique works well for people to get access and observe plant cells in an embryo. These techniques can also be extended to other similar densely packed 3D data. Second, we explored the visualization and interaction de- sign to combine the 3D visualizations of a plant embryo with its associated 2D hierarchy tree. We designed a system with such combinations for biologists to examine the plant cells and record the development history in the hierarchy tree. We support the hierarchy building in two directions, both constructing the history top-down using the lasso selection in 3D environment and bottom-up as the traditional workflow does in the hierarchy tree. We also added a neural network model to give predictions about the assignments for biologists to start with. We conducted an evaluation with biologists, which showed that both 3D and 2D representations help with making decisions, and the tool can inspire insights for them. One main drawback was that the performance of the machine learning model was not ideal. Thus, to assist the process and enhance the model performance, in an improved version of our system, we trained five different ML models and visualized the predictions and their associated uncertainty. We performed a study, and the results indicated that our designed ML representations are easy to understand, and that the new tool can effectively improve the efficiency of assigning the cell lineage

AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we investigate how techniques in Latent Space Cartography can be used to display abstract and representational 2D visualisations of manifolds.Additionally, we present a multi-task metric learning model to capture in its output representations as many metrics as is available in a multi-faceted fine-grained change detection dataset. We also implement an interactive visualisation tool that utilises cartographic techniques that allow dimensions and annotations of graphs to be representative of the underlying factors affecting individual scenarios the user can morph and transform to focus on an individual/sub-group to see how they are performing with respect to said metrics.

Abstract Self-excited high-frequency combustion instabilities were investigated in a 42-injector cryogenic rocket combustor under representative conditions. In previous research it was found that the instabilities are connected to acoustic resonance of the shear-coaxial injectors. In order to gain a better understanding of the flame dynamics during instabilities, an optical access window was realised in the research combustor. This allowed 2D visualisation of supercritical flame response to acoustics under conditions similar to those found in European launcher engines. Through the window, high-speed imaging of the flame was conducted. Dynamic Mode Decomposition was applied to analyse the flame dynamics at specific frequencies, and was able to isolate the flame response to injector or combustion chamber acoustic modes. The flame response at the eigenfrequencies of the oxygen injectors showed symmetric and longitudinal wave-like structures on the dense oxygen core. With the gained understanding of the BKD coupling mechanism it was possible to derive LOX injector geometry changes in order to reduce the risks of injection-coupled instabilities for future cryogenic rocket engines.

Abstract We present a fast prototyping tool for the 3D visualisation and subsequent finite element simulation of 3D CMOS micro-electro-mechanical systems (MEMS). Our process emulation method takes as input the 2D mask layout in CIF format, together with the corresponding process description, and generates a cell representation of the 3D device. The cell representation correctly reflects the layer structure of the device in the direction perpendicular to the wafer surface. Our emulation tool MemCel currently runs in the Mathematica environment, enabling the efficient conversion of 2D design data to 3D representation good for visualisation and simulation. We illustrate the technique for typical surface micromachined and interconnect devices.