868 resultados para Networks analysis
Resumo:
This paper presents a new method to analyze timeinvariant linear networks allowing the existence of inconsistent initial conditions. This method is based on the use of distributions and state equations. Any time-invariant linear network can be analyzed. The network can involve any kind of pure or controlled sources. Also, the transferences of energy that occur at t=O are determined, and the concept of connection energy is introduced. The algorithms are easily implemented in a computer program.
Resumo:
This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern.
Resumo:
Les approches multimodales dans l'imagerie cérébrale non invasive sont de plus en plus considérées comme un outil indispensable pour la compréhension des différents aspects de la structure et de la fonction cérébrale. Grâce aux progrès des techniques d'acquisition des images de Resonance Magnetique et aux nouveaux outils pour le traitement des données, il est désormais possible de mesurer plusieurs paramètres sensibles aux différentes caractéristiques des tissues cérébraux. Ces progrès permettent, par exemple, d'étudier les substrats anatomiques qui sont à la base des processus cognitifs ou de discerner au niveau purement structurel les phénomènes dégénératifs et développementaux. Cette thèse met en évidence l'importance de l'utilisation d'une approche multimodale pour étudier les différents aspects de la dynamique cérébrale grâce à l'application de cette approche à deux études cliniques: l'évaluation structurelle et fonctionnelle des effets aigus du cannabis fumé chez des consommateurs réguliers et occasionnels, et l'évaluation de l'intégrité de la substance grise et blanche chez des jeunes porteurs de la prémutations du gène FMR1 à risque de développer le FXTAS (Fragile-X Tremor Ataxia Syndrome). Nous avons montré que chez les fumeurs occasionnels de cannabis, même à faible concentration du principal composant psychoactif (THC) dans le sang, la performance lors d'une tâche visuo-motrice est fortement diminuée, et qu'il y a des changements dans l'activité des trois réseaux cérébraux impliqués dans les processus cognitifs: le réseau de saillance, le réseau du contrôle exécutif, et le réseau actif par défaut (Default Mode). Les sujets ne sont pas en mesure de saisir les saillances dans l'environnement et de focaliser leur attention sur la tâche. L'augmentation de la réponse hémodynamique dans le cortex cingulaire antérieur suggère une augmentation de l'activité introspective. Une investigation des ef¬fets au niveau cérébral d'une exposition prolongée au cannabis, montre des changements persistants de la substance grise dans les régions associées à la mémoire et au traitement des émotions. Le niveau d'atrophie dans ces structures corrèle avec la consommation de cannabis au cours des trois mois précédant l'étude. Dans la deuxième étude, nous démontrons des altérations structurelles des décennies avant l'apparition du syndrome FXTAS chez des sujets jeunes, asymptomatiques, et porteurs de la prémutation du gène FMR1. Les modifications trouvées peuvent être liées à deux mécanismes différents. Les altérations dans le réseau moteur du cervelet et dans la fimbria de l'hippocampe, suggèrent un effet développemental de la prémutation. Elles incluent aussi une atrophie de la substance grise du lobule VI du cervelet et l'altération des propriétés tissulaires de la substance blanche des projections afférentes correspondantes aux pédoncules cérébelleux moyens. Les lésions diffuses de la substance blanche cérébrale peu¬vent être un marquer précoce du développement de la maladie, car elles sont liées à un phénomène dégénératif qui précède l'apparition des symptômes du FXTAS. - Multimodal brain imaging is becoming a leading tool for understanding different aspects of brain structure and function. Thanks to the advances in Magnetic Resonance imaging (MRI) acquisition schemes and data processing techniques, it is now possible to measure different parameters sensitive to different tissue characteristics. This allows for example to investigate anatomical substrates underlying cognitive processing, or to disentangle, at a pure structural level degeneration and developmental processes. This thesis highlights the importance of using a multimodal approach for investigating different aspects of brain dynamics by applying this approach to two clinical studies: functional and structural assessment of the acute effects of cannabis smoking in regular and occasional users, and grey and white matter assessment in young FMR1 premutation carriers at risk of developing FXTAS. We demonstrate that in occasional smokers cannabis smoking, even at low concentration of the main psychoactive component (THC) in the blood, strongly decrease subjects' performance on a visuo-motor tracking task, and globally alters the activity of the three brain networks involved in cognitive processing: the Salience, the Control Executive, and the Default Mode networks. Subjects are unable to capture saliences in the environment and to orient attention to the task; the increase in Hemodynamic Response in the Anterior Cingulate Cortex suggests an increase in self-oriented mental activity. A further investigation on long term exposure to cannabis, shows a persistent grey matter modification in brain regions associated with memory and affective processing. The degree of atrophy in these structures also correlates with the estimation of drug use in the three months prior the participation to the study. In the second study we demonstrate structural changes in young asymptomatic premutation carriers decades before the onset of FXTAS that might be related to two different mechanisms. Alteration of the cerebellar motor network and of the hippocampal fimbria/ fornix, may reflect a potential neurodevelopmental effect of the premutation. These include grey matter atrophy in lobule VI and modification of white matter tissue property in the corresponding afferent projections through the Middle Cerebellar Peduncles. Diffuse hemispheric white matter lesions that seem to appear closer to the onset of FXTAS and be related to a neurodegenerative phenomenon may mark the imminent onset of FXTAS.
Resumo:
Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.
Resumo:
Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.
Resumo:
The identification of chemical mechanism that can exhibit oscillatory phenomena in reaction networks are currently of intense interest. In particular, the parametric question of the existence of Hopf bifurcations has gained increasing popularity due to its relation to the oscillatory behavior around the fixed points. However, the detection of oscillations in high-dimensional systems and systems with constraints by the available symbolic methods has proven to be difficult. The development of new efficient methods are therefore required to tackle the complexity caused by the high-dimensionality and non-linearity of these systems. In this thesis, we mainly present efficient algorithmic methods to detect Hopf bifurcation fixed points in (bio)-chemical reaction networks with symbolic rate constants, thereby yielding information about their oscillatory behavior of the networks. The methods use the representations of the systems on convex coordinates that arise from stoichiometric network analysis. One of the methods called HoCoQ reduces the problem of determining the existence of Hopf bifurcation fixed points to a first-order formula over the ordered field of the reals that can then be solved using computational-logic packages. The second method called HoCaT uses ideas from tropical geometry to formulate a more efficient method that is incomplete in theory but worked very well for the attempted high-dimensional models involving more than 20 chemical species. The instability of reaction networks may lead to the oscillatory behaviour. Therefore, we investigate some criterions for their stability using convex coordinates and quantifier elimination techniques. We also study Muldowney's extension of the classical Bendixson-Dulac criterion for excluding periodic orbits to higher dimensions for polynomial vector fields and we discuss the use of simple conservation constraints and the use of parametric constraints for describing simple convex polytopes on which periodic orbits can be excluded by Muldowney's criteria. All developed algorithms have been integrated into a common software framework called PoCaB (platform to explore bio- chemical reaction networks by algebraic methods) allowing for automated computation workflows from the problem descriptions. PoCaB also contains a database for the algebraic entities computed from the models of chemical reaction networks.
Resumo:
Introduces extended Crowds protocols. Optional reading
Resumo:
Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required.
Resumo:
Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.
Resumo:
This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.