Tracking granules on the Sun's surface and reconstructing horizontal velocity fields: I. the CST algorithm

Determination of horizontal velocity fields on the solar surface is crucial for understanding the dynamics of structures like mesogranulation or supergranulation or simply the distribution of magnetic fields. We pursue here the development of a method called CST for coherent structure tracking, which determines the horizontal motion of granules in the field of view. We first devise a generalization of Strous method for the segmentation of images and show that when segmentation follows the shape of granules more closely, granule tracking is less effective for large granules because of increased sensitivity to granule fragmentation. We then introduce the multi-resolution analysis on the velocity field, based on Daubechies wavelets, which provides a view of this field on different scales. An algorithm for computing the field derivatives, like the horizontal divergence and the vertical vorticity, is also devised. The effects from the lack of data or from terrestrial atmospheric distortion of the images are also briefly discussed.Comment: in press in Astronomy and Astrophysics, 9 page

Feature correspondences From Multiple Views of Coplanar Ellipses

International audienceWe address the problem of feature correspondences in images of coplanar ellipses with objective to benefit of robust ellipse fitting algorithm. The main difficulty is the lack of projective invariant points immediately available. Therefore, our key idea is to construct virtual line and point features using the property of tangent invariance under perspective projection. The proposed method requires first a robust detection of ellipse edge points to fit a parametric model on each ellipse. The feature lines are then obtained by computing the 4 bitangents to each couple of ellipses. The points are derived by considering the tangent points and the intersection points between bitangents. Results of experimental studies are presented to demonstrate the reliability and robustness of the feature extraction process. Subpixel accuracy is easily achieved. A real application to camera self-calibration is also described

Surface reconstruction of wear in carpets by using a wavelet edge detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth Images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant

Mixed-strain housing for female C57BL/6, DBA/2, and BALB/c mice: validating a split-plot design that promotes refinement and reduction

Abstract Background Inefficient experimental designs are common in animal-based biomedical research, wasting resources and potentially leading to unreplicable results. Here we illustrate the intrinsic statistical power of split-plot designs, wherein three or more sub-units (e.g. individual subjects) differing in a variable of interest (e.g. genotype) share an experimental unit (e.g. a cage or litter) to which a treatment is applied (e.g. a drug, diet, or cage manipulation). We also empirically validate one example of such a design, mixing different mouse strains -- C57BL/6, DBA/2, and BALB/c -- within cages varying in degree of enrichment. As well as boosting statistical power, no other manipulations are needed for individual identification if co-housed strains are differentially pigmented, so also sparing mice from stressful marking procedures. Methods The validation involved housing 240 females from weaning to 5 months of age in single- or mixed- strain trios, in cages allocated to enriched or standard treatments. Mice were screened for a range of 26 commonly-measured behavioural, physiological and haematological variables. Results Living in mixed-strain trios did not compromise mouse welfare (assessed via corticosterone metabolite output, stereotypic behaviour, signs of aggression, and other variables). It also did not alter the direction or magnitude of any strain- or enrichment-typical difference across the 26 measured variables, or increase variance in the data: indeed variance was significantly decreased by mixed- strain housing. Furthermore, using Monte Carlo simulations to quantify the statistical power benefits of this approach over a conventional design demonstrated that for our effect sizes, the split- plot design would require significantly fewer mice (under half in most cases) to achieve a power of 80 %. Conclusions Mixed-strain housing allows several strains to be tested at once, and potentially refines traditional marking practices for research mice. Furthermore, it dramatically illustrates the enhanced statistical power of split-plot designs, allowing many fewer animals to be used. More powerful designs can also increase the chances of replicable findings, and increase the ability of small-scale studies to yield significant results. Using mixed-strain housing for female C57BL/6, DBA/2 and BALB/c mice is therefore an effective, efficient way to promote both refinement and the reduction of animal-use in research

Dlgap1 knockout mice exhibit alterations of the postsynaptic density and selective reductions in sociability

Abstract The scaffold protein DLGAP1 is localized at the post-synaptic density (PSD) of glutamatergic neurons and is a component of supramolecular protein complexes organized by PSD95. Gain-of-function variants of DLGAP1 have been associated with obsessive-compulsive disorder (OCD), while haploinsufficient variants have been linked to autism spectrum disorder (ASD) and schizophrenia in human genetic studies. We tested male and female Dlgap1 wild type (WT), heterozygous (HT), and knockout (KO) mice in a battery of behavioral tests: open field, dig, splash, prepulse inhibition, forced swim, nest building, social approach, and sucrose preference. We also used biochemical approaches to examine the role of DLGAP1 in the organization of PSD protein complexes. Dlgap1 KO mice were most notable for disruption of protein interactions in the PSD, and deficits in sociability. Other behavioral measures were largely unaffected. Our data suggest that Dlgap1 knockout leads to PSD disruption and reduced sociability, consistent with reports of DLGAP1 haploinsufficient variants in schizophrenia and ASD

Sex differences in mood disorders: Perspectives from humans and rodent models

Mood disorders are devastating, often chronic illnesses characterized by low mood, poor affect, and anhedonia. Notably, mood disorders are approximately twice as prevalent in women compared to men. If sex differences in mood are due to underlying biological sex differences, a better understanding of the biology is warranted to develop better treatment or even prevention of these debilitating disorders. In this review, our goals are to: 1) summarize the literature related to mood disorders with respect to sex differences in prevalence, 2) introduce the corticolimbic brain network of mood regulation, 3) discuss strategies and challenges of modeling mood disorders in mice, 4) discuss mechanisms underlying sex differences and how these can be tested in mice, and 5) discuss how our group and others have used a translational approach to investigate mechanisms underlying sex differences in mood disorders in humans and mice

Multiple Object Tracking of Supported Nanoparticles during in situ Environmental TEM Studies of Nanocatalysts

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Intelligent tracking of catalytic nanoparticles trajectories during in situ ETEM experiments

SSCI-VIDE+MEME+TEPInternational audienceCharacterizing at high spatial resolution nanoparticles (NPs) involved in heterogeneous catalysis is a key step for understanding their potential efficiency in catalytic processes. Using Environmental Transmission Electron Microscopy (ETEM), it is now possible to study these nanocatalysts under reactive conditions, i.e. under gas and temperature, approaching experimental working conditions corresponding to their conditioning or activation. With such in situ or operando studies, the reasons for deactivation of the catalyst, for example the growth of the NPs either by Ostwald ripening or coalescence, can be investigated. Recording series of images at reasonable temporal frequencies allows tracking the dynamic evolution of a quite large population of NPs exposed to gas and heat stimuli. A meaningful and quantitative analysis of such observations can then be performed, assuming a correct control or possible damages induced by electron irradiation. To do so, tedious measurements are needed to detect the NPs and identify their trajectories when they possibly move and interact between them by diffusion processes. This is typically a multiple object tracking (MOT) problem, a computer vision approach which has been well-known and treated through automatic routines for a few decades for important societal subjects, such as pedestrians’ localization or traffic survey, see e.g. [1].A robust and automatic pipeline has been derived to achieve this task on the basis of Machine Learning (ML) and MOT approaches applied to the calcination study of Pd NPs supported on delta-alumina, a well-known catalytic system for selective hydrogenation. Details of the ETEM study (FEI/TFS Titan G2 80-300 kV) were reported previously [2]. Sequences of STEM images were acquired during up to 3 hours up to 400°C under oxygen. For the detection of NPs in these micrographs, we used the well-known Unet neural network [3]. Excellent results are obtained once the network has been properly trained on realistic simulated micrographs for which all information is then known a priori. Synthetic images of a given population of spherical (for simplicity) NPs, with known but varying chemical composition and size, can be generated on a supporting media with its own morphological characteristics (variable thickness, rugosity and pore distribution). We also developed a random walk routine to simulate dynamic sequences by moving NPs while respecting expected size and intensity variations during coalescence, crossing or disappearance events.To fine-tune the network, slightly more sophisticated simulations were also performed.The second step of the approach concerns the NP tracking. We have implemented additional features to the algorithm developed by Milan et al. [1] initially dedicated to the tracking of pedestrian walks. It consists in a continuous energy minimization (CEM) which accounts for energy terms representing various events mainly unrealistic in the case of humans or vehicles:(i) fusion (coalescence) of two particles into a single one(ii) less probable but still possible, division of one particle into smaller ones(iii) crossing of particles lying on upper and lower surfaces of the substrateAccording to the above, size and intensity energies criteria were added to the CEM algorithm to identify and confirm fusion / division events.Results of the Unet-based identification of NPs and of the modified CEM tracking approach will be illustrated on both simulated and experimental dynamic sequences [4].[1] A. Milan et al., PAMI, 38 10 (2016) 2054.[2] T. Epicier et al., Catalysis Today, 334, 15 (2019), 68.[3] 0. Ronneberger et al., Lect. Notes Comput. Sci. 9351 (2015) 234.[4] The authors thank the EUR SLEIGHT https://manutech-sleight.com/) and the CNRS Fedreation IngeLySE for financial support, CLYM (www.clym.fr) for the access to the ETEM and IFPEN (Solaize, F) for providing samples