Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Experimental and numerical characterization of anisotropic damage evolution of forged Al6061-T6 alloy

AbstractAluminum alloy 6061-T6 (Al-Mg-Si) has been selected as the material of the vessel for the construction of Jules-Horowitz material testing reactor. Fracture mechanism of this alloy has been investigated using mechanical testing of smooth and notched tensile specimens loaded in different directions. A strong anisotropic fracture behavior has been observed. Microstructural studies using tomography and image analysis have shown a presence of anisotropic distributed coarse precipitates which is the key microstructural feature affecting the damage evolution. These observations were complemented by investigations on fractured tensile samples. A damage scenario of anisotropic growth and coalescence of voids is proposed to explain the fracture behavior associated with the distribution of precipitates. A GTN (Gurson-Tvergaard-Needleman) damage model is used to simulate this scenario and to predict damage evolution

Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources

When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are available. Specifically, we apply our models to analyze the spatial variation of risk of two forms of scrapie infection affecting sheep in Wales (UK) using three surveillance sources on each disease. We first model each disease separately from the combined data sources and then extend our approach to jointly analyze diseases and data sources. We assess the predictive performances of several nested joint models through pseudo cross-validatory predictive model checks

A historical overview of design analyses and experimental observations of ratcheting phenomenon

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Potential impacts of radon, terrestrial gamma and cosmic rays on childhood leukemia in France A quantitative risk assessment

International audiencePrevious epidemiological studies and quantitative risk assessments (QRA) have suggested that natural background radiation may be a cause of childhood leukemia. The present work uses a QRA approach to predict the excess risk of childhood leukemia in France related to three components of natural radiation radon, cosmic rays and terrestrial gamma rays, using excess relative and absolute risk models proposed by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Both models were developed from the Life Span Study (LSS) of Japanese A-bomb survivors. Previous risk assessments were extended by considering uncertainties in radiation-related leukemia risk model parameters as part of this process, within a Bayesian framework. Estimated red bone marrow doses cumulated during childhood by the average French child due to radon, terrestrial gamma and cosmic rays are 4.4, 7.5 and 4.3 mSv, respectively. The excess fractions of cases (expressed as percentages) associated with these sources of natural radiation are 20 % [95 % credible interval (CI) 0-68 %] and 4 % (95 % CI 0-11 %) under the excess relative and excess absolute risk models, respectively. The large CIs, as well as the different point estimates obtained under these two models, highlight the uncertainties in predictions of radiation-related childhood leukemia risks. These results are only valid provided that models developed from the LSS can be transferred to the population of French children and to chronic natural radiation exposures, and must be considered in view of the currently limited knowledge concerning other potential risk factors for childhood leukemia. Last, they emphasize the need for further epidemiological investigations of the effects of natural radiation on childhood leukemia to reduce uncertainties and help refine radiation protection standards. © 2013 Springer-Verlag Berlin Heidelberg

Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach

International audienceThe potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI <0.1-4) leukemia case (all in excess of risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols. © 2013 Springer-Verlag Berlin Heidelberg

Production of functional full-length recombinant human C1q

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Oral vaccination with Liporale™-BCG induces CD4⁺ T cell cytokine production in the lung.

<p>Lymphocytes from the lungs of naïve, Liporale™-BCG vaccinated (BCG oral) or subcutaneous vaccinated (BCG s.c.) mice were stimulated for 6 hours <i>in vitro</i> in the presence of Brefeldin A and monensin then analyzed by flow cytometry. (A) Representative plots show CD4⁺ T cells from the lungs of naïve or BCG vaccinated mice expressing IFNγ, TNFα or IL-2. (B) Bar graphs show the percentage of CD4⁺ T cells from the lungs of naïve or BCG vaccinated mice expressing cytokines at 4, 8 or 30 weeks post immunization. Results are displayed as mean + SEM of n = 5 for each group, significance expressed relative to naïve: *p<0.05, **p<0.01, ***p<0.001 (one way ANOVA with Tukey post test). Eight and 30 weeks results are representative of 2 independent experiments.</p