Disruption of the peritrophic matrix by exogenous chitinase feeding reduces fecundity in Lutzomyia longipalpis females

Lutzomyia longipalpis is the most important vector of visceral leishmaniasis in Brazil. When female sandflies feed on blood, a peritrophic matrix (PM) is formed around the blood bolus. The PM is secreted by midgut cells and composed of proteins, glycoproteins and chitin microfibrils. The PM functions as both a physical barrier against pathogens present in the food bolus and blood meal digestion regulator. Previous studies of mosquitoes and sandflies have shown that the absence of a PM, resulting from adding an exogenous chitinase to the blood meal, accelerates digestion. In the present study, we analysed biological factors associated with the presence of a PM in L. longipalpis females. Insects fed blood containing chitinase (BCC) accelerated egg-laying relative to a control group fed blood without chitinase. However, in the BCC-fed insects, the number of females that died without laying eggs was higher and the number of eggs laid per female was lower. The eggs in both groups were viable and generated adults. Based on these data, we suggest that the absence of a PM accelerates nutrient acquisition, which results in premature egg production and oviposition; however, the absence of a PM reduces the total number of eggs laid per female. Reduced fecundity in the absence of a PM may be due to inefficient nutrient conversion or the loss of the protective role of the PM.

Lentiviral Vector Tropism In Degenerating Retinas

Introduction: In normal mice, lentiviral vector (LV) shows a great efficiency to infect the RPE cells, but transduces retinal neurons more efficiently during development. Here, we investigated the tropism of LV in the degenerating retina of mice, knowing that the retina structure changes during degeneration. We postulated that the viral transduction would be increased by the alteration of the interphotoreceptor matrix (IPM). We tested two different LV-pseudotypes using the VSVG and the Mokola envelopes. Methods: Subretinal injections were performed in wild-type (C57/Bl6) and rhodopsin knockout (Rho-/-) mice. We injected LV-VSVG-EFS-GFPII into 3.3-4.9 month old mice and LV-VSVG-Rho-GFP into 1-1.4 month old mice to target the photoreceptors (PR). LV-MOK-CMV-GFP was injected into 2.4-3.3 months old mice. We sacrificed the animals one week post injection, used immunohistochemistry to identify the transduced cells, and investigated the OLM integrity. Results: Using LV-VSVG-EFS-GFPII into 3.3-4.9 months mice, we observed significant retinal and RPE transduction in Rho-/- mice. However, the retinas showed transduction mainly at the injection's site. We mostly observed GFP+ cells having a Müller cell morphology. Using LV-MOK-CMV-GFP into 2.4-3.3 months mice, we evidenced the same pattern of viral infection, but with more Müller cells targeted by the virus. Using LV-VSVG-Rho-GFP into 1-1.4 month old mice, we don't note any difference between Rho-/- and wild-type mice for transduced cells. The IPM stained with ZO1 appears irregular into the 4.9 months old Rho-/- mice; for the youngest mice (Rho-/- and C57/Bl6), there is no modification of the IPM. Conclusion: The degeneration improves retinal cells transduction due to the alteration of the IPM in old Rho-/- mice. Müller cells seem (by morphological evidences) to be the principal cells expressing the transgene. The LV with Mokola envelope can transduce Müller cells in a degenerating retina with an intact IPM. In 1 month old mice, the degeneration doesn't enhance the transduction in rod PR probably because the IPM is not yet altered. The possibility to target photoreceptors at a later stage of the degeneration is under investigation.

Implementation of zonal control algorithms in adaptive optical systems with sparse control matrices

Miralls deformables més i més grans, amb cada cop més actuadors estan sent utilitzats actualment en aplicacions d'òptica adaptativa. El control dels miralls amb centenars d'actuadors és un tema de gran interès, ja que les tècniques de control clàssiques basades en la seudoinversa de la matriu de control del sistema es tornen massa lentes quan es tracta de matrius de dimensions tan grans. En aquesta tesi doctoral es proposa un mètode per l'acceleració i la paral.lelitzacó dels algoritmes de control d'aquests miralls, a través de l'aplicació d'una tècnica de control basada en la reducció a zero del components més petits de la matriu de control (sparsification), seguida de l'optimització de l'ordenació dels accionadors de comandament atenent d'acord a la forma de la matriu, i finalment de la seva posterior divisió en petits blocs tridiagonals. Aquests blocs són molt més petits i més fàcils de fer servir en els càlculs, el que permet velocitats de càlcul molt superiors per l'eliminació dels components nuls en la matriu de control. A més, aquest enfocament permet la paral.lelització del càlcul, donant una com0onent de velocitat addicional al sistema. Fins i tot sense paral. lelització, s'ha obtingut un augment de gairebé un 40% de la velocitat de convergència dels miralls amb només 37 actuadors, mitjançant la tècnica proposada. Per validar això, s'ha implementat un muntatge experimental nou complet , que inclou un modulador de fase programable per a la generació de turbulència mitjançant pantalles de fase, i s'ha desenvolupat un model complert del bucle de control per investigar el rendiment de l'algorisme proposat. Els resultats, tant en la simulació com experimentalment, mostren l'equivalència total en els valors de desviació després de la compensació dels diferents tipus d'aberracions per als diferents algoritmes utilitzats, encara que el mètode proposat aquí permet una càrrega computacional molt menor. El procediment s'espera que sigui molt exitós quan s'aplica a miralls molt grans.

Use of human MAR elements to improve retroviral vector production.

Retroviral vectors have many favorable properties for gene therapies, but their use remains limited by safety concerns and/or by relatively lower titers for some of the safer self-inactivating (SIN) derivatives. In this study, we evaluated whether increased production of SIN retroviral vectors can be achieved from the use of matrix attachment region (MAR) epigenetic regulators. Two MAR elements of human origin were found to increase and to stabilize the expression of the green fluorescent protein transgene in stably transfected HEK-293 packaging cells. Introduction of one of these MAR elements in retroviral vector-producing plasmids yielded higher expression of the viral vector RNA. Consistently, viral titers obtained from transient transfection of MAR-containing plasmids were increased up to sixfold as compared with the parental construct, when evaluated in different packaging cell systems and transfection conditions. Thus, use of MAR elements opens new perspectives for the efficient generation of gene therapy vectors.

Lentiviral vector-mediated gene transfer in adult mouse photoreceptors is impaired by the presence of a physical barrier

RESUME L'utilisation de la thérapie génique dans l'approche d'un traitement des maladies oculaires dégénératives, plus particulièrement de la rétinite pigmentaire, semble être très prometteuse (Acland et al. 2001). Parmi les vecteurs développés, les vecteurs lentiviraux (dérivé du virus humain HIV-1), permettent la transduction des photorécepteurs après injection sous-rétinienne chez la souris durant les premiers jours de vie. Cependant l'efficacité du transfert de gène est nettement plus limitée dans ce type cellulaire après injection chez l'adulte (Kostic et al. 2003). L'objet de notre étude est de déterminer si la présence d'une barrière physique produite au cours du développement, située entre les photorécepteurs et l'épithélium pigmentaire ainsi qu'entre les photorécepteurs eux-mêmes, est responsable de: la diminution de l'entrée en masse du virus dans les photorécepteurs, minimisant ainsi son efficacité chez la souris adulte. De précédentes recherches, chez le lapin, ont décrit la capacité d'enzymes spécifiques comme la Chondroïtinase ABC et la Neuraminidase X de modifier la structure de la matrice entourant les photorécepteurs (Inter Photoreceptor Matrix, IPM) par digestion de certains de ses constituants suite à leur injection dans l'espace sous-rétinien (Yao et al. 1990). Considérant l'IPM comme une barrière physique, capable de réduire l'efficacité de transduction des photorécepteurs chez la souris adulte, nous avons associé différentes enzymes simultanément à l'injection sous-rétinienne de vecteurs lentiviraux afin d'améliorer la transduction virale en fragilisant I'IPM, la rendant ainsi plus perméable à la diffusion du virus. L'injection sous-rétinienne de Neuraminidase X et de Chondroïtinase ABC chez la souris induit des modifications structurales de l'IPM qui se manifestent respectivement par la révélation ou la disparition de sites de liaison de la peanut agglutinin sur les photorécepteurs. L'injection simultanée de Neuraminidase X avec le vecteur viral contenant le transgène thérapeutique augmente significativement le nombre de photorécepteurs transduits (environ cinq fois). Nous avons en fait démontré que le traitement enzymatique augmente principalement la diffusion du lentivirus dans l'espace situé entre l'épithélium pigmentaire et les photorécepteurs. Le traitement à la Chondroïtinase ABC n'entraîne quant à elle qu'une légère amélioration non significative de la transduction. Cette étude montre qu'une meilleure connaissance de l'IPM ainsi que des substances capables de la modifier (enzymes, drogues etc.) pourrait aider à élaborer de nouvelles stratégies afin d'améliorer la distribution de vecteurs viraux dans la rétine adulte.

The role of extracellular matrix macromolecules in cancer and diabetic macroangiopathy – with special reference to decorin and hyaluronan

The central role of extracellular matrix (ECM) macromolecules in diseases such as cancer and atherosclerotic vascular diseases including diabetic macroangiopathy is indisputable. Decorin and hyaluronan (HA) represent vital ECM macromolecules in the microenvironment of cells and are centrally involved in human cancer and cardiovascular biology. In cancer, decorin is considered to play a tumor suppressive role. However, there is some discrepancy whether malignant cells express it. Regarding HA, its contribution to the development of atherosclerotic vascular diseases has been well established. Nevertheless, the precise role of HA in arterial narrowing associated with diabetes is not known. The present study focused on two vital ECM macromolecules, namely decorin and HA. First, decorin expression was studied in human tumorigenesis. Furthermore, the effect of adenovirus-mediated decorin transduction on selected cancer cell lines was investigated. The results invariably showed that cancer cells completely lacked decorin expression. The study also demonstrated that transducing cancer cells with decorin adenoviral vector markedly inhibited their malignant behavior. In line with this, a strong induction of decorin expression in normal human embryonic stem cells (hESCs), but not in abnormal hESCs was observed during their differentiation. Secondly, the significance of HA in the development of diabetic macroangiopathy in response to hyperglycemia was evaluated. Results showed that the synthesis of HA by vascular smooth muscle cells was significantly increased in response to high glucose concentration. This increase was associated with the diminished ability of the cells to contract collagen-rich matrix suggesting that HA participates in the disturbed vascular remodeling of diabetic patients. The results of this study support endeavours to develop novel ECM macromolecule -based therapies targeting cancer and cardiovascular diseases.

Solving large sparse linear systems over the field GF(2)

Nowadays problem of solving sparse linear systems over the field GF(2) remain as a challenge. The popular approach is to improve existing methods such as the block Lanczos method (the Montgomery method) and the Wiedemann-Coppersmith method. Both these methods are considered in the thesis in details: there are their modifications and computational estimation for each process. It demonstrates the most complicated parts of these methods and gives the idea how to improve computations in software point of view. The research provides the implementation of accelerated binary matrix operations computer library which helps to make the progress steps in the Montgomery and in the Wiedemann-Coppersmith methods faster.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.

A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.

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Exam questions and solutions in PDF

MA167exam01

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A singular vector perspective of 4DVAR: The spatial structure and evolution of baroclinic weather systems

The extent to which the four-dimensional variational data assimilation (4DVAR) is able to use information about the time evolution of the atmosphere to infer the vertical spatial structure of baroclinic weather systems is investigated. The singular value decomposition (SVD) of the 4DVAR observability matrix is introduced as a novel technique to examine the spatial structure of analysis increments. Specific results are illustrated using 4DVAR analyses and SVD within an idealized 2D Eady model setting. Three different aspects are investigated. The first aspect considers correcting errors that result in normal-mode growth or decay. The results show that 4DVAR performs well at correcting growing errors but not decaying errors. Although it is possible for 4DVAR to correct decaying errors, the assimilation of observations can be detrimental to a forecast because 4DVAR is likely to add growing errors instead of correcting decaying errors. The second aspect shows that the singular values of the observability matrix are a useful tool to identify the optimal spatial and temporal locations for the observations. The results show that the ability to extract the time-evolution information can be maximized by placing the observations far apart in time. The third aspect considers correcting errors that result in nonmodal rapid growth. 4DVAR is able to use the model dynamics to infer some of the vertical structure. However, the specification of the case-dependent background error variances plays a crucial role.

A singular vector perspective of 4D-Var: Filtering and interpolation

Four-dimensional variational data assimilation (4D-Var) combines the information from a time sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrievals. It is shown that the 4D-Var analysis increments can be written as a linear combination of the singular vectors of a matrix which is a function of both the observational and the forecast model systems. This formulation is used to consider the filtering and interpolating aspects of 4D-Var using idealized case-studies based on a simple model of baroclinic instability. The results of the 4D-Var case-studies exhibit the reconstruction of the state in unobserved regions as a consequence of the interpolation of observations through time. The results also exhibit the filtering of components with small spatial scales that correspond to noise, and the filtering of structures in unobserved regions. The singular vector perspective gives a very clear view of this filtering and interpolating by the 4D-Var algorithm and shows that the appropriate specification of the a priori statistics is vital to extract the largest possible amount of useful information from the observations. Copyright © 2005 Royal Meteorological Society