Toto-Cell: A new software to analyze cellular events during video-microscopy.

Video-microscopy is a technology widely used to follow, in a single cell manner, cell behavior. A number of new studies are searching a way to track these behaviors by artificial intelligence; unfortunately some real-time events still have to be track manually. For that reason, we developed a software that helps the experimenter to analyze collected data. Toto-cell is very simple to use and it can be adapted at different type of analyses or treatments. It allows a wide new range of parameters that were nearly impossible to calculate only by hand. We thus developed this new software using HEC-1-A endometrial cell line to track different cellular parameters such as: the number of normal/abnormal mitosis, the ratio per day of death, mitosis, cell fusions or finally the length between two mitosis cycles. We treated our cells with cisplatin, doxorubicin or AZD5363 (an Akt inhibitor) to obtain different cellular events. What emerged is a huge heterogeneity for these analyzed parameters between the cells in a single treatment which is clearly demonstrated by the results provided by Toto-Cell. In conclusion, our software is an important tool to facilitate the analysis of video-microscopy, in a quantifying and qualifying manner. It enables a higher accuracy when compared to manual calculations.

Stopping Criterion during Rendering of Computer-Generated Images Based on SVD-Entropy.

The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculation process. Unlike natural-scene images, however, a reference image is not available for computer-generated images. Thus, classic methods to assess noise quantity and stopping criterion during the rendering process are not usable. This is particularly important in the case of global illumination methods based on stochastic techniques: They provide photo-realistic images which are, however, corrupted by stochastic noise. This noise can be reduced by increasing the number of paths, as proved by Monte Carlo theory, but the problem of finding the right number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. Until now, the features taking part in the human evaluation of image quality and the remaining perceived noise are not precisely known. Synthetic image generation tends to be very expensive and the produced datasets are high-dimensional datasets. In that case, finding a stopping criterion using a learning framework is a challenging task. In this paper, a new method for characterizing computational noise for computer generated images is presented. The noise is represented by the entropy of the singular value decomposition of each block composing an image. These Singular Value Decomposition (SVD)-entropy values are then used as input to a recurrent neural network architecture model in order to extract image noise and in predicting a visual convergence threshold of different parts of any image. Thus a new no-reference image quality assessment is proposed using the relation between SVD-Entropy and perceptual quality, based on a sequence of distorted images. Experiments show that the proposed method, compared with experimental psycho-visual scores, demonstrates a good consistency between these scores and stopping criterion measures that we obtain.

Incidence of leptospirosis in the French armed forces from 2004 to 2018: Retrospective analysis.

BACKGROUND: The French military personnel may be exposed to leptospirosis during their training or on duty on the field in continental France, and most of all, in intertropical areas in the French departments and in Africa. The aim of this study was to assess the incidence of leptospirosis from epidemiological surveillance and cases data from 2004 to 2018, and to propose tools to assess leptospirosis risk prior to any mission or leisure activity. METHOD: A retrospective epidemiological study on leptospirosis cases among French Armed Forces was conducted. More data were collected for 2 clusters in Martinique, as most of leptospirosis cases among French military personnel were identified in Martinique. RESULTS: Eighty-eight cases of leptospirosis were reported, 15 cases in continental France and 73 cases in overseas (including 42 cases in the French West Indies). The global leptospirosis incidence rate in continental France was 0.3/100,000 person-years and in overseas 24/100,000 person-years with the higher incidence rate in Martinique (99/100,000 person-years) and in Mayotte (36.9/100,000 person-years). For the clusters in Martinique, between January and June 2009, 7 cases were declared; between 2016 and 2018, 16 cases were reported, high proportions of severe cardiac, renal and neurological forms (6/16) and hospitalizations (9/16). CONCLUSION: The occupational risk is real in French Armed Forces, particularly in malaria-free intertropical areas where chemoprophylaxis by doxycycline is not applied. Prevention can be optimized by the use of practical tools such as tables and cartographies, leading to a better leptospirosis risk assessment and application of preventive recommendations.