A análise de uma população em longo prazo permite a identificação da ausência ou presença de variações nos padrões de ciclo de vida, de distribuição espaço-temporal e dinâmica populacional. O ciclo de vida do camarão sete-barbas Xiphopenaeus kroyeri foi investigado na Enseada de Ubatuba, litoral norte do Estado de São Paulo (23º25\' - 23º27\'S / 45º00\' - 45º03\' W) ao longo de um intervalo de treze anos. Amostras de material biológico, de água de fundo e de sedimento foram coletadas em quatro pontos de amostragem em três períodos de 12 meses cada: janeiro a dezembro de 1998, julho de 2006 a junho de 2007 e setembro de 2010 a agosto de 2011. Dados obtidos em 1999 foram incluídos para as análises de crescimento individual, de longevidade e de idade de maturação. Altas abundâncias de fêmeas maduras e de jovens foram respectivamente associadas a valores de temperatura da água de fundo acima e abaixo da média da região. Indivíduos adultos predominaram em pontos com variação temporal da composição granulométrica do sedimento enquanto que os jovens se estabeleceram naqueles em que o sedimento permaneceu fino entre os períodos. Em 1998, a abundância de jovens, o peso individual de machos e fêmeas de todas as categorias demográficas, a idade de maturação morfológica e fisiológica e a longevidade foram maiores do que nos outros períodos. Os parâmetros da função de crescimento de machos e fêmeas variaram entre os períodos, sem apresentarem uma tendência temporal clara. Os padrões anuais de reprodução e recrutamento foram bastante semelhantes entre os períodos. As alterações nos fatores ambientais provocadas por variações na dinâmica de massas de água, pela ocorrência de El Niño e alterações na intensidade de pesca foram consideradas como possíveis agentes relacionadas às variações nos padrões do ciclo de vida da espécie na região / Long-term studies on a population provide detection of the presence or absence of interannual variation in patterns of its life cycle, spatio-temporal distribution, and dynamics. This study investigate the population of the seabob shrimp Xiphopenaeus kroyeri within a period of 13 years in Ubatuba Bay, northern coast of São Paulo state, Brazil (23º25\' - 23º27\'S / 45º00\' - 45º03\' W). Shrimp, bottom water and sediment samples were collected from four sampling sites during three 12-month periods: January-December 1998, July 2006-June 2007, and September 2010-August 2011. Monthly data collected in 1999 were included in growth, longevity and maturation age analyses. High abundance of mature females and juveniles were associated to the occurrence of temperature values below and above the region average, respectively. Adults predominated in sites that showed temporal variation of sediment texture while juveniles occurred in sites where sediment remained fine between the study periods. In the 1998 period values of juveniles were more abundant, individual weight of all demographic categories was higher, both morphological and physiological maturity was attained later and lifespan was longer than on the following periods. Growth function parameters of males and females varied between periods with no clear temporal trend. Monthly patterns of reproduction and recruitment were similar between the study periods. Changes in the environmental conditions caused by variations in the dynamics of the local water masses, occurrence of El Niño and fishery intensity were considered as possible factors related to the interannual variations in the life cycle pattern of X. kroyeri in the study region

Por causa da grande quantidade de dados de trajetórias producidos por dispositivos móveis, existe um aumento crescente das necessidades de mecanismos para extrair conhecimento a partir desses dados. A maioria dos trabalhos existentes focam nas propriedades geometricas das trajetorias, mas recentemente surgiu o conceito de trajetórias semânticas, nas quais a informação da geografia por baixo da trajetória é integrada aos pontos da trajetória. Nesse novo conceito, trajetórias são observadas como um conjunto de stops e moves, onde stops são as partes mais importantes da trajetória. Os stops e moves são computados pela intersecção das trajetórias com o conjunto de objetos geográficos dados pelo usuário. Nessa dissertação será apresentada uma solução alternativa a descoberta de stops, com a capacidade de achar lugares de interesse que não são esperados pelo usuário. A solução proposta é um método de clusterização espaço-temporal, baseado na velocidade, para ser aplicado em uma trajetória. Foram comparadas duas abordagens diferentes com experimentos baseados em dados reais e mostrado que a computação de stops usando o conceito de velocidade pode ser interessante para várias applicações. / Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concepts of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersection of trajectories with a set of geographic objects given by the user. In this dissertation we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.

Made available in DSpace on 2015-04-11T14:03:15Z (GMT). No. of bitstreams: 1 DISSERTACAO DIEGO FRONER.pdf: 2152405 bytes, checksum: e2cee67bca7f2b5d58460ec7502da76c (MD5) Previous issue date: 2012-02-29 / Fundação de Amparo à Pesquisa do Estado do Amazonas / This work presents the use of different sensors improving the information to perform spatio-temporal alignment of sequential images. The existing proposals called feature-based uses the dynamics of scenes as a major indicator that simultaneously events are ocurring at the same time, and lately indicating their relative position in space. Adding motion sensors to a multiple video cameras system with overlapping fields of coverage, it s possible to acquire information about positions of the monitored objects that serves to aid the alignment between images. / Este trabalho apresenta a utilização de diferentes sensores no aprimoramento da informação necessária para realizar o alinhamento espaço-temporal de imagens sequenciais. As propostas existentes chamadas feature-based utilizam-se da dinâmica da cena como maior indicador de que eventos estão ocorrendo simultaneamente no tempo, e assim posteriormente indicando suas posições relativas no espaço. Adicionando sensores de movimentação a um sistema com múltiplas câmeras de vídeo que possuam sobreposição de campos de cobertura, é possível adquirir informações de posicionamento dos objetos monitorados, servindo assim de auxílio para o alinhamento entre as imagens.

A raiva é uma zoonose viral que afeta o Sistema Nervoso Central (SNC) causando encefalite e meningoencefalite, de evolução aguda e fatal, que acomete mamíferos carnívoros e morcegos, e periodicamente se manifesta sob a forma de epizootias ou surtos epidêmicos em populações humanas. Neste estudo foram analisadas 16.190 amostras de bovinos, equídeos e morcegos, e menos frequentemente de outros mamíferos durante o período de 1981 a 2012, provenientes do Estado do Paraná. Desse total, 2.766 amostras foram positivas para raiva; 81,74% foram de bovinos, 10,34% de equídeos, 4,05% de morcegos, 2,31% em animais de produção não bovinos, 1,52% em caninos e 0,04%em outros animais. Ao longo da série histórica, há, para os bovinos, uma tendência de aumento das notificações e não foram observadas variação sazonal e cíclica. Na análise espaço-temporal foi detectado um aglomerado mais provável de notificações de raiva em bovinos, envolvendo 20 municípios da região litorânea e metropolitana de Curitiba entre 1981 e 1987. Além dele, foram detectados seis aglomerados secundários sugerindo uma migração da raiva ao longo do tempo no Estado do Paraná. Ao longo da série histórica dos equídeos há uma tendência de diminuição das notificações e não foram observadas variação sazonal e cíclica. Os clusters encontrados na análise espaço-temporal da raiva nos equídeos corroboram com aqueles encontrados na análise dos bovinos localizados nas mesmas regiões durante no mesmo período, sugerindo a migração do vírus da raiva no mesmo sentido da observada na análise dos bovinos. Durante o período de 1981 a 1997, os casos de raiva em morcegos acompanham o trajeto da migração dos aglomerados dos bovinos e dos equídeos, o que demonstra que a raiva ocorre endemicamente no território do Estado do Paraná em herbívoros e morcegos. / Rabies is a viral zoonosis that affects the central nervous system (CNS) causing encephalitis and meningoencephalitis, acute and fatal outcome, which affects mammalian carnivores and bats, and periodically manifests itself in the form of epidemics or outbreaks in human populations. In this study 16,190 samples of cattle, horses and bats, and less frequently other mammals were analyzed during the period 1981 to 2012, from the State of Paraná. Of this total, 2,766 samples were positive for rabies; 81.74 % were bovine, equine 10.34 %, 4.05 % of bats, 2.31 % in livestock no bovine, 1.52 % in canine, and 0.04% in other animals. Throughout the time series, there is, for cattle, a trend of increased reporting and no seasonal or cyclical variations were observed. In spatio-temporal analysis, more likely to notifications of rabies in cattle, a cluster involving 20 municipalities in coastal and metropolitan Curitiba between 1981 and 1987 was detected. Besides this, six sub clusters were detected suggesting a migration of anger over time in the state of Paraná. Throughout the historical series of equine there is a downward trend in notifications and no seasonal and cyclical variations were observed. Clusters found in the spatio-temporal analysis of rabies in horses corroborate those found in the analysis of cattle located in the same regions during the same period, suggesting the migration of rabies virus in the same direction as that observed in the cattle analysis. During the period from 1981 to 1997, cases of rabies in bats follow the migration path of clusters of bovine and equine. This shows that rabies is endemic in the state of Paraná in herbivores and bats.

In video transmission at low bit rates the challenge is to compress the video with a minimal reduction of the percieved quality. The compression can be adapted to knowledge of which regions in the video sequence are of most interest to the viewer. Region of interest (ROI) video coding uses this information to control the allocation of bits to the background and the ROI. The aim is to increase the quality in the ROI at the expense of the quality in the background. In order for this to occur the typical content of an ROI for a particular application is firstly determined and the actual detection is performed based on this information. The allocation of bits can then be controlled based on the result of the detection. In this licenciate thesis existing methods to control bit allocation in ROI video coding are investigated. In particular pre-processing methods that are applied independently of the codec or standard. This makes it possible to apply the method directly to the video sequence without modifications to the codec. Three filters are proposed in this thesis based on previous approaches. The spatial filter that only modifies the background within a single frame and the temporal filter that uses information from the previous frame. These two filters are also combined into a spatio-temporal filter. The abilities of these filters to reduce the number of bits necessary to encode the background and to successfully re-allocate these to the ROI are investigated. In addition the computational compexities of the algorithms are analysed. The theoretical analysis is verified by quantitative tests. These include measuring the quality using both the PSNR of the ROI and the border of the background, as well as subjective tests with human test subjects and an analysis of motion vector statistics. The qualitative analysis shows that the spatio-temporal filter has a better coding efficiency than the other filters and it successfully re-allocates the bits from the foreground to the background. The spatio-temporal filter gives an improvement in average PSNR in the ROI of more than 1.32 dB or a reduction in bitrate of 31 % compared to the encoding of the original sequence. This result is similar to or slightly better than the spatial filter. However, the spatio-temporal filter has a better performance, since its computational complexity is lower than that of the spatial filter.

Spatio-temporal reasoning is the area of automated reasoning about space and time and is important in the field of robotics. It is desirable for an autonomous robot to have the ability to reason about both time and space. ST0 is a logic that allows for such reasoning by, among other things, defining a formalism used to describe the relationship between spatial regions and a calculus that allows for deducing further information regarding such spatial relations. An extension of ST0 is ST1 that can be used to describe the relationship between spatial entities across time-points (inter-temporal relations) while ST0 is constrained to doing so within a single time-point. This allows for a better ability of expressing how spatial entities change over time. A major obstacle in using ST1 in practise however, is the fact that any observations made regarding spatial relations between regions is constrained to the time-point in which the observation was made, so we are unable to observe inter-temporal relations. Further complicating things is the fact that deducing such inter-temporal relations is not possible without a frame of reference. This thesis examines one method of overcoming these problems by considering the concept of rigid regions which are assumed to always be unchanging and using them as the frame of reference, or as landmarks. The effectiveness of this method is studied by conducting experiments where a comparison is made between various landmark ratios with respect to the total number of regions under consideration. Results show that when a high degree of intra-temporal relations are fully or partially known, increasing the number of landmark regions will reduce the percentage of inter-temporal relations to be completely unknown. Despite this, very few inter-temporal relations can be fully determined even with a high ratio of landmark regions.

Responsable d’un quart des accidents vasculaires cérébraux, la fibrillation auriculaire (FA) est l’arythmie cardiaque la plus répandue. La thérapie d’ablation par cathéter (CA) est de plus en plus utilisée pour traiter la FA, mais ses effets sur le substrat cardiaque ne sont pas suffisamment compris, d’où un taux de réussite très variable. L’électrocardiogramme (ECG) à 12 voies représente un outil non invasif peu coûteux pour caractériser la FA à partir de l’activité électrique du cœur. Cependant, les prédicteurs classiques de l’issue de la CA présentent plusieurs inconvénients, notamment leur calcul manuel sur une seule voie de l’ECG. Cette thèse exploite explicitement le caractère multi-capteur de l’ECG au moyen de techniques de décomposition multivariées, démontrant qu’elles peuvent améliorer la puissance prédictive de certaines propriétés de l’ECG dans le cadre de la CA. L’amplitude des ondes fibrillatoires est corrélée avec le résultat de la CA, et traitée par une méthode multi-capteur basée sur l’analyse en composantes principales (PCA). Des variantes comme la PCA pondérée (WPCA) et la factorisation en matrices non négatives (NMF) peuvent aussi quantifier la variabilité spatio-temporelle de la FA sur l’ECG. La théorie de l’information permet également d’estimer le niveau de corrélation entre les voies de l’ECG, mis en relation avec le résultat de la CA grâce à des approches multi-capteurs. Enfin, une dernière ligne de recherche concerne la réponse ventriculaire manifestée sur la variabilité cardiaque. L’approche paramétrique de processus ponctuel est capable de souligner certaines propriétés de cette variabilité, améliorant ainsi la caractérisation de la FA. / Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice, and one of the main causes of stroke. Yet its thorough characterization and treatment remain an open issue. Despite the increasing popularity of the radiofrequency catheter ablation (CA) therapy, very little is known about its impact on heart substrate, leading to rather uncertain success rates. This calls for advanced signal processing tools for quantitatively assessing CA outcome. The surface 12-lead electrocardiogram (ECG), a noninvasive and cost-effective cardiac activity recording modality, provides valuable information about AF. However, some issues affect most of the standard CA outcome predictors, e.g., manual computation and limited single-lead perspective. This thesis aims at explicitly exploiting the ECG’s multilead character through multivariate decomposition tools, so as to enhance the role of some ECG features as CA outcome predictors. Fibrillatory wave amplitude is correlated with CA success in a multilead framework through principal component analysis (PCA). Multivariate approaches also enhance AF spatiotemporal variability measured on the ECG (e.g., weighted PCA, nonnegative matrix factorization), evidencing that the less repetitive the AF pattern, the less likely CA success. Information theory also quantifies interlead similarity between AF patterns, and is linked with CA outcome in a multilead framework. Another perspective focuses on the ventricular response as reflected on heart rate variability (HRV). Point process modeling can highlight certain HRV properties typical of AF in a parametric probabilistic context, helping AF pattern recognition.

Stroke is a major cause of disability and death worldwide. Although different clinical studies and trials used Magnetic Resonance Imaging (MRI) to examine patterns of change in different imaging modalities (eg: perfusion and diffusion), we still lack a clear and definite answer to the question: “How does an acute ischemic stroke lesion grow?” The inability to distinguish viable and dead tissue in abnormal MR regions in stroke patients weakens the evidence accumulated to answer this question, and relying on static snapshots of patient scans to fill in the spatio-temporal gaps by “thinking/guessing” make it even harder to tackle. Different opposing observations undermine our understanding of ischemic stroke evolution, especially at the acute stage: viable tissue transiting into dead tissue may be clear and intuitive, however, “visibly” dead tissue restoring to full recovery is still unclear. In this thesis, we search for potential answers to these raised questions from a novel dynamic modelling perspective that would fill in some of the missing gaps in the mechanisms of stroke evolution. We divided our thesis into five parts. In the first part, we give a clinical and imaging background on stroke and state the objectives of this thesis. In the second part, we summarize and review the literature in stroke and medical imaging. We specifically spot gaps in the literature mainly related to medical image analysis methods applied to acute-subacute ischemic stroke. We emphasize studies that progressed the field and point out what major problems remain. Noticeably, we have discovered that macroscopic (imaging-based) dynamic models that simulate how stroke lesion evolves in space and time were completely overlooked: an untapped potential that may alter and hone our understanding of stroke evolution. Progress in the dynamic simulation of stroke was absent –if not inexistent. In the third part, we answer this new call and apply a novel current-based dynamic model âpreviously applied to compare the evolution of facial characteristics between Chimpanzees and Bonobos [Durrleman 2010] – to ischemic stroke. This sets a robust numerical framework and provides us with mathematical tools to fill in the missing gaps between MR acquisition time points and estimate a four-dimensional evolution scenario of perfusion and diffusion lesion surfaces. We then detect two characteristics of patterns of abnormal tissue boundary change: spatial, describing the direction of change –outward as tissue boundary expands or inward as it contracts–; and kinetic, describing the intensity (norm) of the speed of contracting and expanding ischemic regions. Then, we compare intra- and inter-patients estimated patterns of change in diffusion and perfusion data. Nevertheless, topology change limits this approach: it cannot handle shapes with different parts that vary in number over time (eg: fragmented stroke lesions, especially in diffusion scans, which are common). In the fourth part, we suggest a new mathematical dynamic model to increase rigor in the imaging-based dynamic modeling field as a whole by overcoming the topology-change hurdle. Metamorphosis. It morphs one source image into a target one [Trouvé 2005]. In this manuscript, we extend it into dealing with more than two time-indexed images. We propose a novel extension of image-to-image metamorphosis into longitudinal metamorphosis for estimating an evolution scenario of both scattered and solitary ischemic lesions visible on serial MR. It is worth noting that the spatio-temporal metamorphosis we developed is a generic model that can be used to examine intensity and shape changes in time-series imaging and study different brain diseases or disorders. In the fifth part, we discuss our main findings and investigate future directions to explore to sharpen our understanding of ischemia evolution patterns.

Human cognition involves dynamic neural activities in distributed brain areas. For studying such neural mechanisms, magnetoencephalography (MEG) and electroencephalography (EEG) are two important techniques, as they non-invasively detect neural activities with a high temporal resolution. Recordings by MEG/EEG sensors can be approximated as a linear transformation of the neural activities in the brain space (i.e., the source space). However, we only have a limited number sensors compared with the many possible locations in the brain space; therefore it is challenging to estimate the source neural activities from the sensor recordings, in that we need to solve the underdetermined inverse problem of the linear transformation. Moreover, estimating source activities is typically an intermediate step, whereas the ultimate goal is to understand what information is coded and how information flows in the brain. This requires further statistical analysis of source activities. For example, to study what information is coded in different brain regions and temporal stages, we often regress neural activities on some external covariates; to study dynamic interactions between brain regions, we often quantify the statistical dependence among the activities in those regions through “connectivity” analysis. Traditionally, these analyses are done in two steps: Step 1, solve the linear problem under some regularization or prior assumptions, (e.g., each source location being independent); Step 2, do the regression or connectivity analysis. However, biases induced in the regularization in Step 1 can not be adapted in Step 2 and thus may yield inaccurate regression or connectivity results. To tackle this issue, we present novel one-step methods of regression or connectivity analysis in the source space, where we explicitly modeled the dependence of source activities on the external covariates (in the regression analysis) or the cross-region dependence (in the connectivity analysis), jointly with the source-to-sensor linear transformation. In simulations, we observed better performance by our models than by commonly used two-step approaches, when our model assumptions are reasonably satisfied. Besides the methodological contribution, we also applied our methods in a real MEG/EEG experiment, studying the spatio-temporal neural dynamics in the visual cortex. The human visual cortex is hypothesized to have a hierarchical organization, where low-level regions extract low-level features such as local edges, and high-level regions extract semantic features such as object categories. However, details about the spatio-temporal dynamics are less understood. Here, using both the two-step and our one-step regression models in the source space, we correlated neural responses to naturalistic scene images with the low-level and high-level features extracted from a well-trained convolutional neural network. Additionally, we also studied the interaction between regions along the hierarchy using the two-step and our one-step connectivity models. The results from the two-step and the one-step methods were generally consistent; however, the one-step methods demonstrated some intriguing advantages in the regression analysis, and slightly different patterns in the connectivity analysis. In the consistent results, we not only observed an early-to-late shift from low-level to high-level features, which support feedforward information flow along the hierarchy, but also some novel evidence indicating non-feedforward information flow (e.g., topdown feedback). These results can help us better understand the neural computation in the visual cortex. Finally, we compared the empirical sensitivity between MEG and EEG in this experiment, in detecting dependence between neural responses and visual features. Our results show that the less costly EEG was able to achieve comparable sensitivity with that in MEG when the number of observations was about twice of that in MEG. These results can help researchers empirically choose between MEG and EEG when planning their experiments with limited budgets.

Computational models of complex biological systems can provide a better understanding of how living systems function but need to be validated before they are employed for real-life (e.g. clinical) applications. One of the most frequently employed in silico approaches for validating such models is model checking. Traditional model checking approaches are limited to uniscale non-spatial computational models because they do not explicitly distinguish between different scales, and do not take properties of (emergent) spatial structures (e.g. density of multicellular population) into account. This thesis defines a novel multiscale multidimensional spatio-temporal meta model checking methodology which enables validating multiscale (spatial) computational models of biological systems relative to how both numeric (e.g. concentrations) and spatial system properties are expected to change over time and across multiple scales. The methodology has two important advantages. First it supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to produce them. Secondly the methodology is generic because it can be automatically reconfigured according to case study specific types of spatial structures and properties using the meta model checking approach. In addition the methodology could be employed for multiple domains of science, but we illustrate its applicability here only against biological case studies. To automate the computational model validation process, the approach was implemented in software tools, which are made freely available online. Their efficacy is illustrated against two uniscale and four multiscale quantitative computational models encoding phase variation in bacterial colonies and the chemotactic aggregation of cells, respectively the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. This novel model checking approach will enable the efficient construction of reliable multiscale computational models of complex systems.