Développement d’une méthode de simulation multi-échelle pour l’étude des grandes transformations dans les protéines

Les films de simulations qui accompagnent le document ont été réalisés avec Pymol.

Caracterização multi-escalar de bandas de deformação nas tectonossequências paleozóica, pré- e sin-rifte da Bacia do Araripe, Nordeste do Brasil

Deformation bands are structures, developed in porous sandstones, that has small offsets and they are not shown on seismic section. The deformation bands of the pre and synrift sandstones of Araripe Basin were studied in outcrop, macroscopic and microscopic scales. The hierarchical, cinematic and spatial-geometric characteristics, and also the deformational mechanisms acting during its structural evolution were established too. In general, the mesoscopic scale observation allowed to discriminate deformation bands as singles or clusters in three main sets: NNE-SSW dextral; NE-SW normal (sometimes with strike-slip offset); and E-W sinistral; further a bed-parallel deformation bands as a local set. The microscopic characterization allowed to recognize the shearing and cataclastic character of such structures. Through the multi-scale study done in this work we verified that deformation bands analyzed were preferentially developed when sandstones under advanced stage of lithification. We also infer that the geometrical-spatial complexity of these bands, together with the presence of cataclastic matrix, can difficult the migration of fluids in reservoir rocks, resulting on their compartmentalization. Therefore, the study of deformation bands can aid researches about the structural evolution of sedimentary basin, as well as collaborate to understand the hydrodynamic behavior of reservoirs compartmented by these deformational structures

Sistemas orbitais e aéreos aplicados à análise multi-escala da tectônica rúptil atuante na borda sudeste da Bacia do Parnaíba

The tectonics activity on the southern border of Parnaíba Basin resulted in a wide range of brittle structures that affect siliciclastic sedimentary rocks. This tectonic activity and related faults, joints, and folds are poorly known. The main aims of this study were (1) to identify lineaments using several remotesensing systems, (2) to check how the interpretation based on these systems at several scales influence the identification of lineaments, and (3) to contribute to the knowledge of brittle tectonics in the southern border of the Parnaíba Basin. The integration of orbital and aerial systems allowed a multi-scale identification, classification, and quantification of lineaments. Maps of lineaments were elaborated in the following scales: 1:200,000 (SRTM Shuttle Radar Topographic Mission), 1:50,000 (Landsat 7 ETM+ satellite), 1:10,000 (aerial photographs) and 1:5,000 (Quickbird satellite). The classification of the features with structural significance allowed the determination of four structural sets: NW, NS, NE, and EW. They were usually identified in all remote-sensing systems. The NE-trending set was not easily identified in aerial photographs but was better visualized on images of medium-resolution systems (SRTM and Landsat 7 ETM+). The same behavior characterizes the NW-trending. The NS-and EW-trending sets were better identified on images from high-resolution systems (aerial photographs and Quickbird). The structural meaning of the lineaments was established after field work. The NEtrending set is associated with normal and strike-slip faults, including deformation bands. These are the oldest structures identified in the region and are related to the reactivation of Precambrian basement structures from the Transbrazilian Lineament. The NW-trending set represents strike-slip and subordinated normal faults. The high dispersion of this set suggests a more recent origin than the previous structures. The NW-trending set may be related to the Picos-Santa Inês Lineament. The NS-and EW-trending sets correspond to large joints (100 m 5 km long). The truncation relationships between these joint sets indicate that the EW-is older than the NS-trending set. The methodology developed by the present work is an excellent tool for the understanding of the regional and local tectonic structures in the Parnaíba basin. It helps the choice of the best remote-sensing system to identify brittle features in a poorly known sedimentary basin

Influências multi-escala da paisagem e limiar da fragmentação em morcegos no cerrado

Pós-graduação em Ciências Biológicas (Zoologia) - IBRC

Aspetti di sostenibilità in agricoltura a diverse scale spaziali e temporali. Valutazioni ambientali, economiche ed energetiche.

La Tesi analizza le relazioni tra i processi di sviluppo agricolo e l’uso delle risorse naturali, in particolare di quelle energetiche, a livello internazionale (paesi in via di sviluppo e sviluppati), nazionale (Italia), regionale (Emilia Romagna) e aziendale, con lo scopo di valutare l’eco-efficienza dei processi di sviluppo agricolo, la sua evoluzione nel tempo e le principali dinamiche in relazione anche ai problemi di dipendenza dalle risorse fossili, della sicurezza alimentare, della sostituzione tra superfici agricole dedicate all’alimentazione umana ed animale. Per i due casi studio a livello macroeconomico è stata adottata la metodologia denominata “SUMMA” SUstainability Multi-method, multi-scale Assessment (Ulgiati et al., 2006), che integra una serie di categorie d’impatto dell’analisi del ciclo di vita, LCA, valutazioni costi-benefici e la prospettiva di analisi globale della contabilità emergetica. L’analisi su larga scala è stata ulteriormente arricchita da un caso studio sulla scala locale, di una fattoria produttrice di latte e di energia elettrica rinnovabile (fotovoltaico e biogas). Lo studio condotto mediante LCA e valutazione contingente ha valutato gli effetti ambientali, economici e sociali di scenari di riduzione della dipendenza dalle fonti fossili. I casi studio a livello macroeconomico dimostrano che, nonostante le politiche di supporto all’aumento di efficienza e a forme di produzione “verdi”, l’agricoltura a livello globale continua ad evolvere con un aumento della sua dipendenza dalle fonti energetiche fossili. I primi effetti delle politiche agricole comunitarie verso una maggiore sostenibilità sembrano tuttavia intravedersi per i Paesi Europei. Nel complesso la energy footprint si mantiene alta poiché la meccanizzazione continua dei processi agricoli deve necessariamente attingere da fonti energetiche sostitutive al lavoro umano. Le terre agricole diminuiscono nei paesi europei analizzati e in Italia aumentando i rischi d’insicurezza alimentare giacché la popolazione nazionale sta invece aumentando.

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Toward an atomistic understanding of the immune synapse:large-scale molecular dynamics simulation of a membrane-embedded TCR-pMHC-CD4 complex

T-cell activation requires interaction of T-cell receptors (TCR) with peptide epitopes bound by major histocompatibility complex (MHC) proteins. This interaction occurs at a special cell-cell junction known as the immune or immunological synapse. Fluorescence microscopy has shown that the interplay among one agonist peptide-MHC (pMHC), one TCR and one CD4 provides the minimum complexity needed to trigger transient calcium signalling. We describe a computational approach to the study of the immune synapse. Using molecular dynamics simulation, we report here on a study of the smallest viable model, a TCR-pMHC-CD4 complex in a membrane environment. The computed structural and thermodynamic properties are in fair agreement with experiment. A number of biomolecules participate in the formation of the immunological synapse. Multi-scale molecular dynamics simulations may be the best opportunity we have to reach a full understanding of this remarkable supra-macromolecular event at a cell-cell junction.

Across-scale patterning of plant-soil-water interactions surrounding tree islands in Southern Everglades landscapes

The freshwater Everglades is a complex system containing thousands of tree islands embedded within a marsh-grassland matrix. The tree island-marsh mosaic is shaped and maintained by hydrologic, edaphic and biological mechanisms that interact across multiple scales. Preserving tree islands requires a more integrated understanding of how scale-dependent phenomena interact in the larger freshwater system. The hierarchical patch dynamics paradigm provides a conceptual framework for exploring multi-scale interactions within complex systems. We used a three-tiered approach to examine the spatial variability and patterning of nutrients in relation to site parameters within and between two hydrologically defined Everglades landscapes: the freshwater Marl Prairie and the Ridge and Slough. Results were scale-dependent and complexly interrelated. Total carbon and nitrogen patterning were correlated with organic matter accumulation, driven by hydrologic conditions at the system scale. Total and bioavailable phosphorus were most strongly related to woody plant patterning within landscapes, and were found to be 3 to 11 times more concentrated in tree island soils compared to surrounding marshes. Below canopy resource islands in the slough were elongated in a downstream direction, indicating soil resource directional drift. Combined multi-scale results suggest that hydrology plays a significant role in landscape patterning and also the development and maintenance of tree islands. Once developed, tree islands appear to exert influence over the spatial distribution of nutrients, which can reciprocally affect other ecological processes.

RNA Virus Evolution: a Cross-scale, Disease Dynamic Perspective

RNA viruses are an important cause of global morbidity and mortality. The rapid evolutionary rates of RNA virus pathogens, caused by high replication rates and error-prone polymerases, can make the pathogens difficult to control. RNA viruses can undergo immune escape within their hosts and develop resistance to the treatment and vaccines we design to fight them. Understanding the spread and evolution of RNA pathogens is essential for reducing human suffering. In this dissertation, I make use of the rapid evolutionary rate of viral pathogens to answer several questions about how RNA viruses spread and evolve. To address each of the questions, I link mathematical techniques for modeling viral population dynamics with phylogenetic and coalescent techniques for analyzing and modeling viral genetic sequences and evolution. The first project uses multi-scale mechanistic modeling to show that decreases in viral substitution rates over the course of an acute infection, combined with the timing of infectious hosts transmitting new infections to susceptible individuals, can account for discrepancies in viral substitution rates in different host populations. The second project combines coalescent models with within-host mathematical models to identify driving evolutionary forces in chronic hepatitis C virus infection. The third project compares the effects of intrinsic and extrinsic viral transmission rate variation on viral phylogenies.

Classification multi-échelle d'images à très haute résolution spatiale basée sur une nouvelle approche texturale

Résumé : Face à l’accroissement de la résolution spatiale des capteurs optiques satellitaires, de nouvelles stratégies doivent être développées pour classifier les images de télédétection. En effet, l’abondance de détails dans ces images diminue fortement l’efficacité des classifications spectrales; de nombreuses méthodes de classification texturale, notamment les approches statistiques, ne sont plus adaptées. À l’inverse, les approches structurelles offrent une ouverture intéressante : ces approches orientées objet consistent à étudier la structure de l’image pour en interpréter le sens. Un algorithme de ce type est proposé dans la première partie de cette thèse. Reposant sur la détection et l’analyse de points-clés (KPC : KeyPoint-based Classification), il offre une solution efficace au problème de la classification d’images à très haute résolution spatiale. Les classifications effectuées sur les données montrent en particulier sa capacité à différencier des textures visuellement similaires. Par ailleurs, il a été montré dans la littérature que la fusion évidentielle, reposant sur la théorie de Dempster-Shafer, est tout à fait adaptée aux images de télédétection en raison de son aptitude à intégrer des concepts tels que l’ambiguïté et l’incertitude. Peu d’études ont en revanche été menées sur l’application de cette théorie à des données texturales complexes telles que celles issues de classifications structurelles. La seconde partie de cette thèse vise à combler ce manque, en s’intéressant à la fusion de classifications KPC multi-échelle par la théorie de Dempster-Shafer. Les tests menés montrent que cette approche multi-échelle permet d’améliorer la classification finale dans le cas où l’image initiale est de faible qualité. De plus, l’étude effectuée met en évidence le potentiel d’amélioration apporté par l’estimation de la fiabilité des classifications intermédiaires, et fournit des pistes pour mener ces estimations.

Large-scale neural dynamics: simple and complex

We review the use of neural field models for modelling the brain at the large scales necessary for interpreting EEG, fMRI, MEG and optical imaging data. Albeit a framework that is limited to coarse-grained or mean-field activity, neural field models provide a framework for unifying data from different imaging modalities. Starting with a description of neural mass models we build to spatially extended cortical models of layered two-dimensional sheets with long range axonal connections mediating synaptic interactions. Reformulations of the fundamental non-local mathematical model in terms of more familiar local differential (brain wave) equations are described. Techniques for the analysis of such models, including how to determine the onset of spatio-temporal pattern forming instabilities, are reviewed. Extensions of the basic formalism to treat refractoriness, adaptive feedback and inhomogeneous connectivity are described along with open challenges for the development of multi-scale models that can integrate macroscopic models at large spatial scales with models at the microscopic scale.

Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape

Overrecentdecades,remotesensinghasemergedasaneffectivetoolforimprov- ing agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi- resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchi- cal segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra- variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a sin- gle image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.