Some notes on fishes in British East Africa and Uganda

Volume: I

EGF increases expression and activity of PAs in preimplantation rat embryos and their implantation rate

BACKGROUND: Embryo implantation plays a major role in embryogenesis and the outcome of pregnancy. Plasminogen activators (PAs) have been implicated in mammalian fertilization, early stages of development and embryo implantation. As in-vitro developing embryos resulted in lower implantation rate than those developed in-vivo we assume that a reduced PAs activity may be involved. In the present work we studied the effect of EGF on PAs activity, quantity and embryo implantation. METHODS: Zygotes were flushed from rat oviducts on day one of pregnancy and grown in-vitro in R1ECM supplemented with EGF (10 ng/ml) and were grown up to the blastocyst stage. The control groups were grown in the same medium without EGF. The distribution and quantity of the PAs were examined using fluorescence immunohistochemistry followed by measurement of PAs activity using the chromogenic assay. Implantation rate was studied using the embryo donation model. RESULTS: PAs distribution in the embryos was the same in EGF treated and untreated embryos. Both PAs were localized in the blastocysts' trophectoderm, supporting the assumption that PAs play a role in the implantation process in rats. EGF increased the quantity of uPA at all stages studied but the 8-cell stage as compared with controls. The tissue type PA (tPA) content was unaffected except the 8-cell stage, which was increased. The activity of uPA increased gradually towards the blastocyst stage and more so due to the presence of EGF. The activity of tPA did not vary with the advancing developmental stages although it was also increased by EGF. The presence of EGF during the preimplantation development doubled the rate of implantation of the treated group as compared with controls

Model-free Consensus Maximization for Non-Rigid Shapes

Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80\%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art.Comment: ECCV1

Model-free Consensus Maximization for Non-Rigid Shapes

Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using ‘rules’ on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art.ISSN:0302-9743ISSN:1611-334

Structured Regularization of Functional Map Computations

International audienceWe consider the problem of non-rigid shape matching using the functional map framework. Specifically, we analyze a commonly used approach for regularizing functional maps, which consists in penalizing the failure of the unknown map to commute with the Laplace-Beltrami operators on the source and target shapes. We show that this approach has certain undesirable fundamental theoretical limitations, and can be undefined even for trivial maps in the smooth setting. Instead we propose a novel, theoretically well-justified approach for regularizing functional maps, by using the notion of the resolvent of the Laplacian operator. In addition, we provide a natural one-parameter family of regularizers, that can be easily tuned depending on the expected approximate isometry of the input shape pair. We show on a wide range of shape correspondence scenarios that our novel regularization leads to an improvement in the quality of the estimated functional, and ultimately pointwise correspondences before and after commonly-used refinement techniques