Sensor network inference from partial and correlated observations

Projecte final de carrera fet en col.laboració amb Ecole Polytechnique Federale de LausanneEnglish: Consider a Wireless Sensor Network (WSN) where links can fail and the structure is varying a lot in function of the application implemented. We design a routing and encoding algorithm robust to arbitrary wireless connection. We also need a distributed data gathering in order to be able to reconstruct the signal from any node in the network and make the encoding and routing process independent from each other. We achieve all this requirements by implementing a gossip algorithm. Using this distributed algorithm to disseminate the information we study the problem of data reconstruction from partial observations in a WSN environment. We analyze two different data cases: In the first one, sensor measurements are scalar magnitudes (temperatures). In the second case we extend the model for high dimensional signals (images). For the first case, we want to reconstruct the temperature field values by observing partial sensor network measurements. Sensors disseminate their observation in network to one randomly chosen neighbour sensor by the gossip algorithm. After a certain number of message exchange cycles that is less than the total number of sensors in the network (under-determined system), we want to guarantee the accurate reconstruction of the network data. We compress the original signal in the data gathering stage using compressive sensing techniques. The obtained inverse problem based on node observations can be solved if we in addition introduce a priory data knowledge (smoothness). Our data is discretized so we develop an algorithm to minimize a non convex function, which is the hardest computational case. We propose a Viterbi List algorithm-based, using different techniques of energy regularization. Moreover, when the dimensions of sensed data are higher we treat the problem from another point of view. We assume that all the sensors participate in the data exchanging process; hence we receive observations from all the nodes in the network. Furthermore, each image is compressed before sending it using compressed sensing techniques. To solve the obtained inverse problem we assume a priory data knowledge of the signal (sparsity) and certain correlation amongst neighbour data. We propose two solutions, the first is a joint total variation (JTV) model based on the classical total variation regularization for the minimization of convex-continuous functions. On the other hand we approach the problem with a Joint matching pursuit algorithm (JMP) based on the l1 minimization of a convex function. For both methods we exploit the correlation amongst neighbour?s views to jointly decode each sensor signal, and improve the performance of the classical methods.Castellano: Consideremos una red de sensores inalámbrica (WNS) donde las conexiones pueden fallar i la estructura de la red varía mucho en función de la aplicación que diseñemos. Hemos implementado un algoritmo para la codificación i enrutamiento de la información que es robusto a las conexiones arbitrarias que se dan en una WNS. Necesitamos también un algoritmo distribuido para recopilación de las distintas medidas de los sensores con el fin de poder reconstruir los datos originales des de cualquier punto de la red i de este modo hacer independientes el proceso de codificación i enrutamiento. Con estos objetivos hemos implementado un algoritmo distribuido para la difusión de los datos a través de la red llamado gossip algorithm. Utilizando este algoritmo distribuido hemos estudiado la reconstrucción de los datos disponiendo solamente de las observaciones de parte de los nodos de la red en un entorno WNS. El estudio se realiza para dos tipos distintos de datos: en el primero las medidas de los sensores son magnitudes escalares (temperaturas) i en el segundo hemos ampliado el modelo para señales multidimensionales (imágenes). En el primer caso, queremos reconstruir los campos de temperatura de todos los sensores teniendo solo parte de las medidas de la red. Los sensores propagan sus datos hacia un sensor vecino elegido aleatoriamente siguiendo el algoritmo gossip. Después de cierto número de ciclos de intercambio de información, que es menor que el número de sensores (sistema indeterminado) queremos garantizar la reconstrucción de todos los datos de la red des de cualquier punto de la misma. Comprimimos los datos en la fase de propagación i adquisición utilizando técnicas de compressive sensing. Se obtiene un problema inverso (inverse problem) que solo se puede solucionar si tenemos algún tipo de información a priori de los datos (variación suave entre vecinos). Las medidas de los sensores son discretizadas por tanto implementamos un algoritmo para minimizar una función no convexa, que es el caso más caro computacionalmente. Proponemos una solución basada en el algoritmo de Viterbi utilizando distintas técnicas de regularización de energía. Por otro lado cuando trabajamos con imágenes abordamos el problema des de otro punto de vista. Asumimos que todos los nodos participan en el proceso de intercambio de datos, por tanto tenemos las observaciones de todos los sensores. Cada imagen es comprimida antes de ser enviada a través de la red utilizando técnicas de compressive sensing. Para solucionar este inverse problem asumimos que los datos son sparse i que existe cierta correlación entre las señales de sensores vecinos. Proponemos dos soluciones, la primera es un modelo de joint total variation (JTV) basado en el modelo clásico de la regularización de la variación total, para la minimización de funciones convexas. La segunda es joint matching pursuit (JMP) basado en la minimización l1 de funciones convexas. Para los dos métodos introducimos la correlación entre sensores vecinos para decodificar conjuntamente cada imagen i así aumentar el rendimiento de los algoritmos clásicos.Català: Considerem una xarxa de sensors sense fils (WSN) on les connexions poden fallar i la estructura de la xarxa varia molt en funció de la aplicació que dissenyem. Hem implementat un algoritme per a la codificació i enrutament de la informació que es robust a les connexions arbitraries que es donen en una WSN. També necessitem un algoritme distribuït per a la recopilació de les diferents mesures dels sensors per tal de poder reconstruir les dades originals des de qualsevol node de la xarxa i així fer que el procés de codificació i enrutament de les dades siguin independents. Amb aquests objectius hem implementat un algoritme distribuït per a la difusió de les dades a traves de la xarxa anomenat gossip algorithm. Utilitzant aquest algoritme distribuït, hem estudiat el problema de la reconstrucció de les dades disposant nomes les observacions de part dels nodes de la xarxa en un entorn WSN. L'estudi es realitza per dos tipus diferents de dades: en el primer les mesures dels sensors son magnituds escalars (temperatures) i en el segon hem ampliat el model per senyals multidimensionals (imatges). En el primer cas, volem reconstruir els camps de temperatura de tots els sensors tenint nomes part de les mesures de la xarxa. Els sensors propaguen les seves dades cap a un sensor veí triat aleatòriament seguint l'algoritme gossip. Desprès d'un cert numero de cicles d'intercanvi d'informació que es menor que el numero total de sensors (sistema indeterminat) volem garantir la reconstrucció acurada de les dades de tota la xarxa des de qualsevol punt de la mateixa. Es a dir, comprimim les dades en la fase de propagació i adquisició de la informació utilitzant tècniques de compressive sensing. S'obté un problema invers (inverse problem) que només pot ser solucionat si assumim algun tipus de coneixement a priori de les dades (variació suau entre veïns). Les mesures dels sensors son discretitzades per tant hem implementat un algoritme per minimitzar una funció no convexa, que es el cas computacionalment mes car. Proposem una solució basada en l'algoritme de Viterbi utilitzant diferents tècniques per a la regularització d'energia. Per altra banda quan treballem amb imatges abordem el problema des d'un altre punt de vista. Assumim que tots els nodes participen en el procés d'intercanvi de dades, per tant tenim les observacions de tots els sensors. Cada imatge es comprimida abans de ser enviada a traves de la xarxa utilitzat tècniques de compressive sensing. Per solucionar aquest invers problem assumim que les dades son sparse i que existeix certa correlació entre les senyals de sensors veïns. Proposem dues solucions, la primera es un model de Joint total variation (JTV) basat en el model clàssic de la regularització de la variació total, per a la minimització de funcions convexes . La segona es Joint matching pursuit (JMP) basat en la minimització l1 de funcions convexes. Pels dos mètodes introduïm la correlació entre sensors veïns per decodificar conjuntament cada imatge i així augmentar el rendiment dels algoritmes clàssics

Vav GEFs are required for β2 integrin-dependent functions of neutrophils

Integrin regulation of neutrophils is essential for appropriate adhesion and transmigration into tissues. Vav proteins are Rho family guanine nucleotide exchange factors that become tyrosine phosphorylated in response to adhesion. Using Vav1/Vav3-deficient neutrophils (Vav1/3ko), we show that Vav proteins are required for multiple β2 integrin-dependent functions, including sustained adhesion, spreading, and complement-mediated phagocytosis. These defects are not attributable to a lack of initial β2 activation as Vav1/3ko neutrophils undergo chemoattractant-induced arrest on intercellular adhesion molecule-1 under flow. Accordingly, in vivo, Vav1/3ko leukocytes arrest on venular endothelium yet are unable to sustain adherence. Thus, Vav proteins are specifically required for stable adhesion. β2-induced activation of Cdc42, Rac1, and RhoA is defective in Vav1/3ko neutrophils, and phosphorylation of Pyk2, paxillin, and Akt is also significantly reduced. In contrast, Vav proteins are largely dispensable for G protein-coupled receptor–induced signaling events and chemotaxis. Thus, Vav proteins play an essential role coupling β2 to Rho GTPases and regulating multiple integrin-induced events important in leukocyte adhesion and phagocytosis

The multifaceted functions of neutrophils.

Neutrophils and neutrophil-like cells are the major pathogen-fighting immune cells in organisms ranging from slime molds to mammals. Central to their function is their ability to be recruited to sites of infection, to recognize and phagocytose microbes, and then to kill pathogens through a combination of cytotoxic mechanisms. These include the production of reactive oxygen species, the release of antimicrobial peptides, and the recently discovered expulsion of their nuclear contents to form neutrophil extracellular traps. Here we discuss these primordial neutrophil functions, which also play key roles in tissue injury, by providing details of neutrophil cytotoxic functions and congenital disorders of neutrophils. In addition, we present more recent evidence that interactions between neutrophils and adaptive immune cells establish a feed-forward mechanism that amplifies pathologic inflammation. These newly appreciated contributions of neutrophils are described in the setting of several inflammatory and autoimmune diseases

The multifaceted functions of neutrophils.

Neutrophils and neutrophil-like cells are the major pathogen-fighting immune cells in organisms ranging from slime molds to mammals. Central to their function is their ability to be recruited to sites of infection, to recognize and phagocytose microbes, and then to kill pathogens through a combination of cytotoxic mechanisms. These include the production of reactive oxygen species, the release of antimicrobial peptides, and the recently discovered expulsion of their nuclear contents to form neutrophil extracellular traps. Here we discuss these primordial neutrophil functions, which also play key roles in tissue injury, by providing details of neutrophil cytotoxic functions and congenital disorders of neutrophils. In addition, we present more recent evidence that interactions between neutrophils and adaptive immune cells establish a feed-forward mechanism that amplifies pathologic inflammation. These newly appreciated contributions of neutrophils are described in the setting of several inflammatory and autoimmune diseases

AKAP9, a regulator of microtubule dynamics, contributes to blood-testis barrier function

The blood-testis barrier (BTB), formed between adjacent Sertoli cells, undergoes extensive remodeling to facilitate the transport of preleptotene spermatocytes across the barrier from the basal to apical compartments of the seminiferous tubules for further development and maturation into spermatozoa. The actin cytoskeleton serves unique structural and supporting roles in this process, but little is known about the role of microtubules and their regulators during BTB restructuring. The large isoform of the cAMP-responsive scaffold protein AKAP9 regulates microtubule dynamics and nucleation at the Golgi. We found that conditional deletion of Akap9 in mice after the initial formation of the BTB at puberty leads to infertility. AKAP9 deletion results in marked alterations in the organization of microtubules in Sertoli cells and a loss of barrier integrity despite a relatively intact, albeit more apically localized F-actin and BTB tight junctional proteins. These changes are accompanied by a loss of haploid spermatids due to impeded meiosis. The barrier, however, progressively reseals in older AKAP9-deficient mice, which correlates with a reduction in germ cell apoptosis and a greater incidence of meiosis. However, spermiogenesis remains defective, suggesting additional roles for AKAP9 in this process. Together, our data suggest that AKAP9 and, by inference, the regulation of the microtubule network are critical for BTB function and subsequent germ cell development during spermatogenesis

FcγRIII Mediates Neutrophil Recruitment to Immune Complexes A Mechanism for Neutrophil Accumulation in Immune-Mediated Inflammation

AbstractNeutrophil accumulation is a hallmark of immune complex-mediated inflammatory disorders. Current models of neutrophil recruitment envision the capture of circulating neutrophils by activated endothelial cells. We now demonstrate that immobilized immune complexes alone support the rapid attachment of neutrophils, under physiologic flow conditions. Initial cell tethering requires the low-affinity Fcγ receptor IIIB (FcγRIIIB), and the β2 integrins are additionally required for the subsequent shear-resistant adhesion. The attachment function of FcγRIIIB may be facilitated by its observed presentation on neutrophil microvilli. In vivo, in a model of acute antiglomerular basement membrane nephritis in which immune complexes are accessible to circulating neutrophils, FcγRIII-deficient mice had a significant reduction in neutrophil recruitment. Thus, the interaction of immune complexes with FcγRIII may mediate early neutrophil recruitment in immune complex-mediated inflammation

A Lupus-Associated Mac-1 Variant Has Defects in Integrin Allostery and Interaction with Ligands under Force

Leukocyte CD18 integrins increase their affinity for ligand by transmitting allosteric signals to and from their ligand-binding αI domain. Mechanical forces induce allosteric changes that paradoxically slow dissociation by increasing the integrin/ligand bond lifetimes, referred to as catch bonds. Mac-1 formed catch bonds with its ligands. However, a Mac-1 gene (ITGAM) coding variant (rs1143679, R77H), which is located in the β-propeller domain and is significantly associated with systemic lupus erythematosus risk, exhibits a marked impairment in 2D ligand affinity and affinity maturation under mechanical force. Targeted mutations and activating antibodies reveal that the failure in Mac-1 R77H allostery is rescued by induction of cytoplasmic tail separation and full integrin extension. These findings demonstrate roles for R77, and the β-propeller in which it resides, in force-induced allostery relay and integrin bond stabilization. Defects in these processes may have pathological consequences, as the Mac-1 R77H variant is associated with increased susceptibility to lupus