The present study evaluated the prevalence of Porphyromonas gingivalis and the correlation between the bacterial culture method and the detection of immunoglobulin A (IgA) specific to theP. gingivalis fimbrial antigen in gingival crevicular fluid (GCF). P. gingivalis was isolated from 78.3% of subgingival plaque samples obtained from active sites and 34.7% of those from inactive sites of periodontal patients. P. gingivalis was isolated from only 4.7% of healthy subjects (control group). Immunoglobulins specific to the P. gingivalis fimbrial antigen were detected by enzyme-linked immunosorbent assay (ELISA). The overall agreement between the results of the P. gingivalis culture method and the results of specific IgA detection in periodontal patients was 71.7% for active sites and 58.7% for inactive sites. IgA specific to P. gingivalis was absent in GCF from all of the sites of healthy subjects. The results suggest that P. gingivalis is associated with the local production of specific IgA. The detection of IgA antibodies specific to P. gingivalis in GCF by ELISA may be used as a predictive parameter to reveal the early phase of the activation of recurrent periodontal infections.
Historical archives save invaluable treasures and play a critical role in the conservation of Cultural Heritage. Old photographs and videos, which have survived over time and stored in these archives, preserve traces of architecture and urban transformation and, in many cases, are the only evidence of buildings that no longer exist. They are a precious source of enormous informative potential in Cultural Heritage documentation and save invaluable treasures. Thanks to photogrammetric techniques it is possible to extract metric information from these sources useful for 3D virtual reconstructions of monuments and historic buildings. This paper explores the ways to search for, classify and group historical data by considering their possible use in metric documentation and aims to provide an overview of criticality and open issues of the methodologies that could be used to process these data. A practical example is described and presented as a case study. The video “Torino 1928”, an old movie dating from the 1930s, was processed for reconstructing the temporary pavilions of the “Exposition” held in Turin in 1928. Despite the initial concerns relating to processing this kind of data, the experimental methodology used in this research has allowed to reach a quality of results of acceptable standard.
The seroprevalence to Toxoplasma gondii (41.1%), rubella virus (88.2%), cytomegalovirus (86.0%), and herpes simplex virus (80.0%) has been evaluated in fertile women living in Catania (Sicily). The population group studied was divided into four age groups to quantify the risk of primary infection in each age group.
Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for the conservation of the memory of the past. However, when the heritage has been lost the only way to recover this information is the use of historical images from archives. The aim of this study is to experiment with new ways to search for architectural heritage in video material and to save the effort of the operator in the archive in terms of efficiency and time. A workflow is proposed to automatically detect lost heritage in film footage using Deep Learning to find suitable images to process with photogrammetry for its 3D virtual reconstruction. The performance of the network was tested on two case studies considering different architectural scenarios, the Tour Saint Jacques which still exists for the tuning of the networks, and Les Halles to test the algorithms on a real case of an architecture which has been destroyed. Despite the poor quantity and low quality of the historical images available for the training of the network, it has been demonstrated that, with few frames, it was possible to reach the same results in terms of performance of a network trained on a large dataset. Moreover, with the introduction of new metrics based on time intervals the measure of the real time saving in terms of human effort was achieved. These findings represent an important innovation in the documentation of destroyed monuments and open new ways to recover information about the past.
<p><strong>Abstract.</strong> Researching historical archives for material suitable for photogrammetry is essential for the documentation and 3D reconstruction of Cultural Heritage, especially when this heritage has been lost or transformed over time. This research presents an innovative workflow which combines the photogrammetric procedure with Machine Learning for the processing of historical film footage. A Neural Network is trained to automatically detect frames in which architectural heritage appears. These frames are subsequently processed using photogrammetry and finally the resulting model is assessed for metric quality. This paper proposes best practises in training and validation on a Cultural Heritage asset. The algorithm was tested through a case study of the Tour Saint Jacques in Paris for which an entirely new dataset was created. The findings are encouraging both in terms of saving human effort and of improvement of the photogrammetric survey pipeline. This new tool can help researchers to better manage and organize historical information.</p>
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