Building information modeling como ferramenta de visualização de realidade aumentada em obras de reabilitação – um caso de estudo

Congresso Construção 2012 - 4º Congresso Nacional/17, 18 e 19 Dezembro

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Development of human central nervous system 3D in vitro models for preclinical research

Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

High strain rate constitutive modeling forhistorical structures subjected to blast loading

Doctoral Thesis Civil Engineering

Fused deposition modeling with polypropylene

This paper addresses the potential of polypropylene (PP) as a candidate for fused deposition modeling (FDM)-based 3D printing technique. The entire filament production chain is evaluated, starting with the PP pellets, filament production by extrusion and test samples printing. This strategy enables a true comparison between parts printed with parts manufactured by compression molding, using the same grade of raw material. Printed samples were mechanically characterized and the influence of filament orientation, layer thickness, infill degree and material was assessed. Regarding the latter, two grades of PP were evaluated: a glass-fiber reinforced and a neat, non-reinforced, one. The results showed the potential of the FDM to compete with conventional techniques, especially for the production of small series of parts/components; also, it was showed that this technique allows the production of parts with adequate mechanical performance and, therefore, does not need to be restricted to the production of mockups and prototypes.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

Disseny d'un Battle Chess 3D (2)

Aquesta memòria descriu el projecte de final de carrera anomenat "Disseny d’un Battle Chess 3D (2)", que tracta de la creació, modelat i animació de peces per a un joc d’escacs en 3 dimensions amb certes temàtiques, i que posteriorment s’integren amb el projecte "Disseny d’un Battle Chess 3D (1)" per a formar un joc interactiu d’escacs en un applet de Java. Es descriuen les eines utilitzades, les fases de creació, tècniques simbòliques, mètodes més emprats, proves sotmeses, limitacions, i finalment s’arriba una conclusió de treball aconseguit.

Stochastic time-concentration activity models for cytotoxicity in 3D brain cell cultures.

BACKGROUND: In vitro aggregating brain cell cultures containing all types of brain cells have been shown to be useful for neurotoxicological investigations. The cultures are used for the detection of nervous system-specific effects of compounds by measuring multiple endpoints, including changes in enzyme activities. Concentration-dependent neurotoxicity is determined at several time points. METHODS: A Markov model was set up to describe the dynamics of brain cell populations exposed to potentially neurotoxic compounds. Brain cells were assumed to be either in a healthy or stressed state, with only stressed cells being susceptible to cell death. Cells may have switched between these states or died with concentration-dependent transition rates. Since cell numbers were not directly measurable, intracellular lactate dehydrogenase (LDH) activity was used as a surrogate. Assuming that changes in cell numbers are proportional to changes in intracellular LDH activity, stochastic enzyme activity models were derived. Maximum likelihood and least squares regression techniques were applied for estimation of the transition rates. Likelihood ratio tests were performed to test hypotheses about the transition rates. Simulation studies were used to investigate the performance of the transition rate estimators and to analyze the error rates of the likelihood ratio tests. The stochastic time-concentration activity model was applied to intracellular LDH activity measurements after 7 and 14 days of continuous exposure to propofol. The model describes transitions from healthy to stressed cells and from stressed cells to death. RESULTS: The model predicted that propofol would affect stressed cells more than healthy cells. Increasing propofol concentration from 10 to 100 μM reduced the mean waiting time for transition to the stressed state by 50%, from 14 to 7 days, whereas the mean duration to cellular death reduced more dramatically from 2.7 days to 6.5 hours. CONCLUSION: The proposed stochastic modeling approach can be used to discriminate between different biological hypotheses regarding the effect of a compound on the transition rates. The effects of different compounds on the transition rate estimates can be quantitatively compared. Data can be extrapolated at late measurement time points to investigate whether costs and time-consuming long-term experiments could possibly be eliminated.

Preliminary regional rockfall hazard mapping using LIDAR-based slope frequency distribution and CONEFALL modeling

A factor limiting preliminary rockfall hazard mapping at regional scale is often the lack of knowledge of potential source areas. Nowadays, high resolution topographic data (LiDAR) can account for realistic landscape details even at large scale. With such fine-scale morphological variability, quantitative geomorphometric analyses become a relevant approach for delineating potential rockfall instabilities. Using digital elevation model (DEM)-based ?slope families? concept over areas of similar lithology and cliffs and screes zones available from the 1:25,000 topographic map, a susceptibility rockfall hazard map was drawn up in the canton of Vaud, Switzerland, in order to provide a relevant hazard overview. Slope surfaces over morphometrically-defined thresholds angles were considered as rockfall source zones. 3D modelling (CONEFALL) was then applied on each of the estimated source zones in order to assess the maximum runout length. Comparison with known events and other rockfall hazard assessments are in good agreement, showing that it is possible to assess rockfall activities over large areas from DEM-based parameters and topographical elements.

Molecular modeling study of the binding and signaling properties of the TCRpMHC complex

The activation of the specific immune response against tumor cells is based on the recognition by the CD8+ Cytotoxic Τ Lymphocytes (CTL), of antigenic peptides (p) presented at the surface of the cell by the class I major histocompatibility complex (MHC). The ability of the so-called T-Cell Receptors (TCR) to discriminate between self and non-self peptides constitutes the most important specific control mechanism against infected cells. The TCR/pMHC interaction has been the subject of much attention in cancer therapy since the design of the adoptive transfer approach, in which Τ lymphocytes presenting an interesting response against tumor cells are extracted from the patient, expanded in vitro, and reinfused after immunodepletion, possibly leading to cancer regression. In the last decade, major progress has been achieved by the introduction of engineered lypmhocytes. In the meantime, the understanding of the molecular aspects of the TCRpMHC interaction has become essential to guide in vitro and in vivo studies. In 1996, the determination of the first structure of a TCRpMHC complex by X-ray crystallography revealed the molecular basis of the interaction. Since then, molecular modeling techniques have taken advantage of crystal structures to study the conformational space of the complex, and understand the specificity of the recognition of the pMHC by the TCR. In the meantime, experimental techniques used to determine the sequences of TCR that bind to a pMHC complex have been used intensively, leading to the collection of large repertoires of TCR sequences that are specific for a given pMHC. There is a growing need for computational approaches capable of predicting the molecular interactions that occur upon TCR/pMHC binding without relying on the time consuming resolution of a crystal structure. This work presents new approaches to analyze the molecular principles that govern the recognition of the pMHC by the TCR and the subsequent activation of the T-cell. We first introduce TCRep 3D, a new method to model and study the structural properties of TCR repertoires, based on homology and ab initio modeling. We discuss the methodology in details, and demonstrate that it outperforms state of the art modeling methods in predicting relevant TCR conformations. Two successful applications of TCRep 3D that supported experimental studies on TCR repertoires are presented. Second, we present a rigid body study of TCRpMHC complexes that gives a fair insight on the TCR approach towards pMHC. We show that the binding mode of the TCR is correctly described by long-distance interactions. Finally, the last section is dedicated to a detailed analysis of an experimental hydrogen exchange study, which suggests that some regions of the constant domain of the TCR are subject to conformational changes upon binding to the pMHC. We propose a hypothesis of the structural signaling of TCR molecules leading to the activation of the T-cell. It is based on the analysis of correlated motions in the TCRpMHC structure. - L'activation de la réponse immunitaire spécifique dirigée contre les cellules tumorales est basée sur la reconnaissance par les Lymphocytes Τ Cytotoxiques (CTL), d'un peptide antigénique (p) présenté à la suface de la cellule par le complexe majeur d'histocompatibilité de classe I (MHC). La capacité des récepteurs des lymphocytes (TCR) à distinguer les peptides endogènes des peptides étrangers constitue le mécanisme de contrôle le plus important dirigé contre les cellules infectées. L'interaction entre le TCR et le pMHC est le sujet de beaucoup d'attention dans la thérapie du cancer, depuis la conception de la méthode de transfer adoptif: les lymphocytes capables d'une réponse importante contre les cellules tumorales sont extraits du patient, amplifiés in vitro, et réintroduits après immunosuppression. Il peut en résulter une régression du cancer. Ces dix dernières années, d'importants progrès ont été réalisés grâce à l'introduction de lymphocytes modifiés par génie génétique. En parallèle, la compréhension du TCRpMHC au niveau moléculaire est donc devenue essentielle pour soutenir les études in vitro et in vivo. En 1996, l'obtention de la première structure du complexe TCRpMHC à l'aide de la cristallographie par rayons X a révélé les bases moléculaires de l'interaction. Depuis lors, les techniques de modélisation moléculaire ont exploité les structures expérimentales pour comprendre la spécificité de la reconnaissance du pMHC par le TCR. Dans le même temps, de nouvelles techniques expérimentales permettant de déterminer la séquence de TCR spécifiques envers un pMHC donné, ont été largement exploitées. Ainsi, d'importants répertoires de TCR sont devenus disponibles, et il est plus que jamais nécessaire de développer des approches informatiques capables de prédire les interactions moléculaires qui ont lieu lors de la liaison du TCR au pMHC, et ce sans dépendre systématiquement de la résolution d'une structure cristalline. Ce mémoire présente une nouvelle approche pour analyser les principes moléculaires régissant la reconnaissance du pMHC par le TCR, et l'activation du lymphocyte qui en résulte. Dans un premier temps, nous présentons TCRep 3D, une nouvelle méthode basée sur les modélisations par homologie et ab initio, pour l'étude de propriétés structurales des répertoires de TCR. Le procédé est discuté en détails et comparé à des approches standard. Nous démontrons ainsi que TCRep 3D est le plus performant pour prédire des conformations pertinentes du TCR. Deux applications à des études expérimentales des répertoires TCR sont ensuite présentées. Dans la seconde partie de ce travail nous présentons une étude de complexes TCRpMHC qui donne un aperçu intéressant du mécanisme d'approche du pMHC par le TCR. Finalement, la dernière section se concentre sur l'analyse détaillée d'une étude expérimentale basée sur les échanges deuterium/hydrogène, dont les résultats révèlent que certaines régions clés du domaine constant du TCR sont sujettes à un changement conformationnel lors de la liaison au pMHC. Nous proposons une hypothèse pour la signalisation structurelle des TCR, menant à l'activation du lymphocyte. Celle-ci est basée sur l'analyse des mouvements corrélés observés dans la structure du TCRpMHC.

Correction for heart rate variability during 3D whole heart MR coronary angiography.

PURPOSE: To evaluate the effect of a real-time adaptive trigger delay on image quality to correct for heart rate variability in 3D whole-heart coronary MR angiography (MRA). MATERIALS AND METHODS: Twelve healthy adults underwent 3D whole-heart coronary MRA with and without the use of an adaptive trigger delay. The moment of minimal coronary artery motion was visually determined on a high temporal resolution MRI. Throughout the scan performed without adaptive trigger delay, trigger delay was kept constant, whereas during the scan performed with adaptive trigger delay, trigger delay was continuously updated after each RR-interval using physiological modeling. Signal-to-noise, contrast-to-noise, vessel length, vessel sharpness, and subjective image quality were compared in a blinded manner. RESULTS: Vessel sharpness improved significantly for the middle segment of the right coronary artery (RCA) with the use of the adaptive trigger delay (52.3 +/- 7.1% versus 48.9 +/- 7.9%, P = 0.026). Subjective image quality was significantly better in the middle segments of the RCA and left anterior descending artery (LAD) when the scan was performed with adaptive trigger delay compared to constant trigger delay. CONCLUSION: Our results demonstrate that the use of an adaptive trigger delay to correct for heart rate variability improves image quality mainly in the middle segments of the RCA and LAD.

State-of-the-art of 3D cultures (organs-on-a-chip) in safety testing and pathophysiology.

Integrated approaches using different in vitro methods in combination with bioinformatics can (i) increase the success rate and speed of drug development; (ii) improve the accuracy of toxicological risk assessment; and (iii) increase our understanding of disease. Three-dimensional (3D) cell culture models are important building blocks of this strategy which has emerged during the last years. The majority of these models are organotypic, i.e., they aim to reproduce major functions of an organ or organ system. This implies in many cases that more than one cell type forms the 3D structure, and often matrix elements play an important role. This review summarizes the state of the art concerning commonalities of the different models. For instance, the theory of mass transport/metabolite exchange in 3D systems and the special analytical requirements for test endpoints in organotypic cultures are discussed in detail. In the next part, 3D model systems for selected organs--liver, lung, skin, brain--are presented and characterized in dedicated chapters. Also, 3D approaches to the modeling of tumors are presented and discussed. All chapters give a historical background, illustrate the large variety of approaches, and highlight up- and downsides as well as specific requirements. Moreover, they refer to the application in disease modeling, drug discovery and safety assessment. Finally, consensus recommendations indicate a roadmap for the successful implementation of 3D models in routine screening. It is expected that the use of such models will accelerate progress by reducing error rates and wrong predictions from compound testing.

TCRep 3D: an automated in silico approach to study the structural properties of TCR repertoires.

TCRep 3D is an automated systematic approach for TCR-peptide-MHC class I structure prediction, based on homology and ab initio modeling. It has been considerably generalized from former studies to be applicable to large repertoires of TCR. First, the location of the complementary determining regions of the target sequences are automatically identified by a sequence alignment strategy against a database of TCR Vα and Vβ chains. A structure-based alignment ensures automated identification of CDR3 loops. The CDR are then modeled in the environment of the complex, in an ab initio approach based on a simulated annealing protocol. During this step, dihedral restraints are applied to drive the CDR1 and CDR2 loops towards their canonical conformations, described by Al-Lazikani et. al. We developed a new automated algorithm that determines additional restraints to iteratively converge towards TCR conformations making frequent hydrogen bonds with the pMHC. We demonstrated that our approach outperforms popular scoring methods (Anolea, Dope and Modeller) in predicting relevant CDR conformations. Finally, this modeling approach has been successfully applied to experimentally determined sequences of TCR that recognize the NY-ESO-1 cancer testis antigen. This analysis revealed a mechanism of selection of TCR through the presence of a single conserved amino acid in all CDR3β sequences. The important structural modifications predicted in silico and the associated dramatic loss of experimental binding affinity upon mutation of this amino acid show the good correspondence between the predicted structures and their biological activities. To our knowledge, this is the first systematic approach that was developed for large TCR repertoire structural modeling.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Zonation for 3D aquifer characterization based on joint inversions of multimethod crosshole geophysical data

Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petro-physical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine the parameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petro-physical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.