Evaluation of chemotherapeutic potential of natural extracts using 3D models of colon cancer

Dissertation to obtain master degree in Biotechnology

Region-Based Feature Interpretation for Recognizing 3D Models in 2D Images

In model-based vision, there are a huge number of possible ways to match model features to image features. In addition to model shape constraints, there are important match-independent constraints that can efficiently reduce the search without the combinatorics of matching. I demonstrate two specific modules in the context of a complete recognition system, Reggie. The first is a region-based grouping mechanism to find groups of image features that are likely to come from a single object. The second is an interpretive matching scheme to make explicit hypotheses about occlusion and instabilities in the image features.

A Newton method for pose refinement of 3D models

Different optimization methods can be employed to optimize a numerical estimate for the match between an instantiated object model and an image. In order to take advantage of gradient-based optimization methods, perspective inversion must be used in this context. We show that convergence can be very fast by extrapolating to maximum goodness-of-fit with Newton's method. This approach is related to methods which either maximize a similar goodness-of-fit measure without use of gradient information, or else minimize distances between projected model lines and image features. Newton's method combines the accuracy of the former approach with the speed of convergence of the latter.

Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

New class of spin projection operators for 3D models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

3D models created from ROV videos and their corresponding subtransect dimensions

Underwater video transects have become a common tool for quantitative analysis of the seafloor. However a major difficulty remains in the accurate determination of the area surveyed as underwater navigation can be unreliable and image scaling does not always compensate for distortions due to perspective and topography. Depending on the camera set-up and available instruments, different methods of surface measurement are applied, which make it difficult to compare data obtained by different vehicles. 3-D modelling of the seafloor based on 2-D video data and a reference scale can be used to compute subtransect dimensions. Focussing on the length of the subtransect, the data obtained from 3-D models created with the software PhotoModeler Scanner are compared with those determined from underwater acoustic positioning (ultra short baseline, USBL) and bottom tracking (Doppler velocity log, DVL). 3-D model building and scaling was successfully conducted on all three tested set-ups and the distortion of the reference scales due to substrate roughness was identified as the main source of imprecision. Acoustic positioning was generally inaccurate and bottom tracking unreliable on rough terrain. Subtransect lengths assessed with PhotoModeler were on average 20% longer than those derived from acoustic positioning due to the higher spatial resolution and the inclusion of slope. On a high relief wall bottom tracking and 3-D modelling yielded similar results. At present, 3-D modelling is the most powerful, albeit the most time-consuming, method for accurate determination of video subtransect dimensions.

The use of close-range photogrammetry in zooarchaeology : Creating accurate 3D models of wolf crania to study dog domestication

Acknowledgements We thank the Muséum National d'Histoire Naturelle, Paris, that provided access to the specimens, and access to the morphometric platform where the surface scans were performed. We also thank Raphael Cornette and Julien Claude for the fruitful discussions we had when writing the manuscript. This work was supported by NERC (grant number NE/K003259/1) and the European Research Council (ERC-2013-StG 337574-UNDEAD). This is publication ISEM 2016-127. We thank the two anonymous reviewers who greatly helped to improve the manuscript.

Porosity imaged by a vector projection algorithm correlates with fractal dimension measured on 3D models obtained by microCT

International audience

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Evaluating biomaterial-and microfluidic-based 3D tumor models

Cancer is a major cause of morbidity and mortality worldwide, with a disease burden estimated to increase in the coming decades. Disease heterogeneity and limited information on cancer biology and disease mechanisms are aspects that 2D cell cultures fail to address. We review the current "state-of-the-art" in 3D Tissue Engineering (TE) models developed for and used in cancer research. Scaffold-based TE models and microfluidics, are assessed for their potential to fill the gap between 2D models and clinical application. Recent advances in combining the principles of 3D TE models and microfluidics are discussed, with a special focus on biomaterials and the most promising chip-based 3D models.

Improvement of 3D protein models using multiple templates guided by single-template model quality assessment

Motivation: Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust2, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method. Results: We find that using accurate local model quality scores to guide alignment selection is the most consistent way to significantly improve models for each of the sequence to structure alignment methods tested. In addition, using accurate global model quality for re-ranking alignments, prior to selection, further improves the majority of multi-template modelling methods tested. Furthermore, subsequent clustering of the resulting population of multiple-template models significantly improves the quality of selected models compared with the previous version of our tertiary structure prediction method, IntFOLD-TS.