A new label fusion method using graph cuts: application to hippocampus segmentation

The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

A proposal for a modular and application-aware autonomic manager of private cloud infrastructures

Recientemente, el paradigma de la computación en la nube ha recibido mucho interés por parte tanto de la industria como del mundo académico. Las infraestructuras cloud públicas están posibilitando nuevos modelos de negocio y ayudando a reducir costes. Sin embargo, una compañía podría desear ubicar sus datos y servicios en sus propias instalaciones, o tener que atenerse a leyes de protección de datos. Estas circunstancias hacen a las infraestructuras cloud privadas ciertamente deseables, ya sea para complementar a las públicas o para sustituirlas por completo. Por desgracia, las carencias en materia de estándares han impedido que las soluciones para la gestión de infraestructuras privadas se hayan desarrollado adecuadamente. Además, la multitud de opciones disponibles ha creado en los clientes el miedo a depender de una tecnología concreta (technology lock-in). Una de las causas de este problema es la falta de alineación entre la investigación académica y los productos comerciales, ya que aquella está centrada en el estudio de escenarios idealizados sin correspondencia con el mundo real, mientras que éstos consisten en soluciones desarrolladas sin tener en cuenta cómo van a encajar con los estándares más comunes o sin preocuparse de hacer públicos sus resultados. Con objeto de resolver este problema, propongo un sistema de gestión modular para infraestructuras cloud privadas enfocado en tratar con las aplicaciones en lugar de centrarse únicamente en los recursos hardware. Este sistema de gestión sigue el paradigma de la computación autónoma y está diseñado en torno a un modelo de información sencillo, desarrollado para ser compatible con los estándares más comunes. Este modelo divide el entorno en dos vistas, que sirven para separar aquello que debe preocupar a cada actor involucrado del resto de información, pero al mismo tiempo permitiendo relacionar el entorno físico con las máquinas virtuales que se despliegan encima de él. En dicho modelo, las aplicaciones cloud están divididas en tres tipos genéricos (Servicios, Trabajos de Big Data y Reservas de Instancias), para que así el sistema de gestión pueda sacar partido de las características propias de cada tipo. El modelo de información está complementado por un conjunto de acciones de gestión atómicas, reversibles e independientes, que determinan las operaciones que se pueden llevar a cabo sobre el entorno y que es usado para hacer posible la escalabilidad en el entorno. También describo un motor de gestión encargado de, a partir del estado del entorno y usando el ya mencionado conjunto de acciones, la colocación de recursos. Está dividido en dos niveles: la capa de Gestores de Aplicación, encargada de tratar sólo con las aplicaciones; y la capa del Gestor de Infraestructura, responsable de los recursos físicos. Dicho motor de gestión obedece un ciclo de vida con dos fases, para así modelar mejor el comportamiento de una infraestructura real. El problema de la colocación de recursos es atacado durante una de las fases (la de consolidación) por un resolutor de programación entera, y durante la otra (la online) por un heurístico hecho ex-profeso. Varias pruebas han demostrado que este acercamiento combinado es superior a otras estrategias. Para terminar, el sistema de gestión está acoplado a arquitecturas de monitorización y de actuadores. Aquella estando encargada de recolectar información del entorno, y ésta siendo modular en su diseño y capaz de conectarse con varias tecnologías y ofrecer varios modos de acceso. ABSTRACT The cloud computing paradigm has raised in popularity within the industry and the academia. Public cloud infrastructures are enabling new business models and helping to reduce costs. However, the desire to host company’s data and services on premises, and the need to abide to data protection laws, make private cloud infrastructures desirable, either to complement or even fully substitute public oferings. Unfortunately, a lack of standardization has precluded private infrastructure management solutions to be developed to a certain level, and a myriad of diferent options have induced the fear of lock-in in customers. One of the causes of this problem is the misalignment between academic research and industry ofering, with the former focusing in studying idealized scenarios dissimilar from real-world situations, and the latter developing solutions without taking care about how they f t with common standards, or even not disseminating their results. With the aim to solve this problem I propose a modular management system for private cloud infrastructures that is focused on the applications instead of just the hardware resources. This management system follows the autonomic system paradigm, and is designed around a simple information model developed to be compatible with common standards. This model splits the environment in two views that serve to separate the concerns of the stakeholders while at the same time enabling the traceability between the physical environment and the virtual machines deployed onto it. In it, cloud applications are classifed in three broad types (Services, Big Data Jobs and Instance Reservations), in order for the management system to take advantage of each type’s features. The information model is paired with a set of atomic, reversible and independent management actions which determine the operations that can be performed over the environment and is used to realize the cloud environment’s scalability. From the environment’s state and using the aforementioned set of actions, I also describe a management engine tasked with the resource placement. It is divided in two tiers: the Application Managers layer, concerned just with applications; and the Infrastructure Manager layer, responsible of the actual physical resources. This management engine follows a lifecycle with two phases, to better model the behavior of a real infrastructure. The placement problem is tackled during one phase (consolidation) by using an integer programming solver, and during the other (online) with a custom heuristic. Tests have demonstrated that this combined approach is superior to other strategies. Finally, the management system is paired with monitoring and actuators architectures. The former able to collect the necessary information from the environment, and the later modular in design and capable of interfacing with several technologies and ofering several access interfaces.

Optimization of Dengue immunoassay by label-free interferometric optical detection method

In this communication we report a direct immunoassay for detecting dengue virus by means of a label-free interferometric optical detection method. We also demonstrate how we can optimize this sensing response by adding a blocking step able to significantly enhance the optical sensing response. The blocking reagent used for this optimization is a dry milk diluted in phosphate buffered saline. The recognition curve of dengue virus over the proposed surface sensor demonstrates the capacity of this method to be applied in Point of Care technology.

Entre: de repente, sin fin, lo innombrable

Etiquetamos para reducir nuestra incertidumbre; clasificamos en base a términos opuestos para poder entender. El universo y el espacio construido en particular, se explican desde una arquitectónica dual: dentro – fuera, arriba – abajo, derecha – izquierda, día – noche, hombre – mujer. Lo que nos diferencia como grupo, raza o género, distinguiendo los modos de vivir, las ideologías y las teorías, es la relación que establecemos entre los términos opuestos: entre lo individual y lo colectivo, lo privado y lo público, la imaginación y la realidad, la identidad y la otredad, el orden y el caos, el deseo y la saciedad. El afán clasificatorio dicotómico de la realidad que la modernidad lleva a su extremo, hiere la vida: la mutua exclusión de los opuestos elimina la distancia, el espacio-tiempo entre ambos, que es donde la vida se sitúa. La concepción dual que rige nuestro conocimiento contamina también la relación entre los arquitectos y la sociedad, siendo la causa del ambiente tóxico que envuelve al habitante que ya no se identifica con los lugares que habita. Puede que sea oportuno pasar del pensamiento binario a una lógica de lo intersticial que abandone la dualidad para instalarse en el Entre; los arquitectos serían mediadores entre el poder y la vida, sintiendo el Entre como el transcurso de la vida y la potencia de interacción: los lugares ambiguos e intermedios, es donde sucede el encuentro entre los términos, entre arquitectos y habitantes, entre la imaginación y la realidad, entre tú y yo. ABSTRACT We label to reduce our uncertainty; we classify based on opposites to understand. The universe and all constructed space in particular, are explained from a dual architecture: inside - out, up - down, right - left, day - night, male - female, black - white. What differentiate us as a group, race, gender, distinguishing the lifestyles, ideologies and theories, is how we draw the relation between the opposites: between the individual and the collective, private and public, imagination and reality , identity and otherness, order and disorder, desire and satiety. The dichotomous classification of reality that modernity carried to its extreme, hurts life: the mutual exclusion of opposites eliminates the distance, the space-time between the two, which is where life is located. The dual conception that governs our knowledge has also contaminated the relationship between architect and society, being the cause of the toxic environment that surrounds the inhabitant who no longer identify himself with the places he inhabit. It may be appropriate pass from binary thinking to a logic of the interstitial which abandon duality to settle in Between; architect then will be the mediator between authorities and life, sensing the Between as a flowing of life a potency of interaction rather than a separation between the extremes; ambiguous, in –between, places are where the encounter between opposites happens, between the architect and the inhabitant, between imagination and reality, between you and me.

The role of private sector in development: The relation between public-private investment in infrastructure and agricultural exports in developing countries

Increasing foreign private investment in developing countries explains why the Public-Private Investment (PPI) is becoming a key tool to reach the development goal. This article analyzes the relation between PPI in infrastructure and agricultural exports in developing countries. We use the panel data approach (52 countries and 17 years). Results show that PPI in infrastructure has a positive impact on agricultural exports of developing countries. The impact is greater in developing countries with higher income rates. This suggests that the lower income countries require the intervention of public sector without which private investment cannot help to economic development.

An Open-Label Investigation of the Pharmacokinetics and Tolerability of Oral Cysteamine in Adults with Cystic Fibrosis

This study was funded by Health Sciences Scotland (West Medical Building, University Avenue, Glasgow G12 8QQ. UK) and the Cystic Fibrosis Trust (One Aldgate, London. EC3N 1RE. UK). The funders did not contribute to study design, data collection, analysis, this report or the decision to publish.

High-frequency, spin-label EPR of nonaxial lipid ordering and motion in cholesterol-containing membranes

The EPR spectra of spin-labeled lipid chains in fully hydrated bilayer membranes of dimyristoyl phosphatidylcholine containing 40 mol % of cholesterol have been studied in the liquid-ordered phase at a microwave radiation frequency of 94 GHz. At such high field strengths, the spectra should be optimally sensitive to lateral chain ordering that is expected in the formation of in-plane domains. The high-field EPR spectra from random dispersions of the cholesterol-containing membranes display very little axial averaging of the nitroxide g-tensor anisotropy for lipids spin labeled toward the carboxyl end of the sn-2 chain (down to the 8-C atom). For these positions of labeling, anisotropic 14N-hyperfine splittings are resolved in the gzz and gyy regions of the nonaxial EPR spectra. For positions of labeling further down the lipid chain, toward the terminal methyl group, the axial averaging of the spectral features systematically increases and is complete at the 14-C atom position. Concomitantly, the time-averaged 〈Azz〉 element of the 14N-hyperfine tensor decreases, indicating that the axial rotation at the terminal methyl end of the chains arises from correlated torsional motions about the bonds of the chain backbone, the dynamics of which also give rise to a differential line broadening of the 14N-hyperfine manifolds in the gzz region of the spectrum. These results provide an indication of the way in which lateral ordering of lipid chains in membranes is induced by cholesterol.