FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

From 2D to 3D supervised segmentation and classification for cultural heritage applications.

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

Different effects of growth hormone releasing peptide (GHRP-6) and GH-releasing hormone on GH release in endogenous and exogenous hypercortisolism

OBJECTIVE Chronic hypercortisolism is associated with decreased OH responsiveness to GHRH, GHRP-6 is a synthetic hexapeptide that releases GH in several species, including man, As GHRH and GHRP-6 apparently stimulate GH release by different mechanisms, we evaluated the ON responses to these peptides in patients with endogenous and exogenous glucocorticoid excess and also in control subjects.DESIGN Six patients with endogenous hypercortisolism, nine with exogenous glucocorticoid excess; and 10 normal controls were submitted to three tests, in random order, with GHRH (100 mu g), GHRP-6 (1 mu g/kg) or GHRP+GHRP-6, in the same doses, i.v., on separate days.MEASUREMENTS GH was measured by immunofluorometric assay, IGF-I was determined by radio immunoassay, Plasma glucose was measured by the glucose-oxidase technique.RESULTS Peak GH values (mean+/-SE; mu g/l) after GHRH were significantly blunted in endogenous (2.0 +/- 0.7) and exogenous (3.6 +/- 1.2) hypercortisolae mic patients compared to controls (24.9 +/- 5.1), the endogenous group had lower peak GH values after GHRP-6 alone (7.7 +/- 1.9) or together with GHRH (18.8 +/- 5.8) than those observed in controls (GHRP-6: 22.1 +/- 3.6; GHRH+GHRP-6: 77.4 +/- 15.0) and in exogenous hypercortisolism (27.4 +/- 6.2 and 78.1 +/- 19.9). There were no differences in the GH responses to GHRP-6 alone or in combination with GHRH when controls were compared to the exogenous group, No changes in plasma IGF-I and glucose levels were observed.CONCLUSIONS Our results suggest that hypercortisolism had a different effect on the ON-releasing mechanisms stimulated by GHRH and GHRP-6. Moreover, in endogenous hypercortisolism both GHRN and GHRP-6 pathways are affected, while in the exogenous group GHRP-6 releasing mechanisms are apparently preserved.Universidade Federal de São Paulo, EPM, UNIFESP, DIV ENDOCRINOL, C POSTAL 20266, BR-04034970 São Paulo, BRAZILUniversidade Federal de São Paulo, EPM, UNIFESP, DIV ENDOCRINOL, C POSTAL 20266, BR-04034970 São Paulo, BRAZILWeb of Scienc

5-azacytidine make human preadipocytes able to differentiate into mesoderm-derived cell lineages

In the present study we have evaluated whether (i) 5-azacytidine (AZA), a well-known demethylating agent, could be able to modify the phenotype of human preadipocytes and (ii) the modified cells could possess multilineage differentiation potential. Human preadipocytes at the 3rd passage were treated for 48 or 96 h with 10 mu M AZA and then expanded up to passage 5. Stem cell markers, such as OCT-4, Nanog, and Sox2, were upregulated after 96 h of treatment with the demethylating treatment. Further, decreases in the expression of genes, such as adipose differentiation-related protein, characterizing the preadipocytes were noted. Our data showed that AZA-treated preadipocytes differentiated into cell lineages derived from mesoderm. Indeed, after incubation with inductive media for 3 weeks, osteblast-, chondrocyte-, and myoblast-like cells were detected in the cultures. Interestingly, both upregulation of stem cell markers and differentiation potential were maintained by the treated cultures expanded until the 5th passage. Taken together, our results suggest that AZA, without the use of transduction methods, convert preadipocytes to a less differentiated state that can be induced, under suitable stimuli, to the formation of mesoderm-derived cell lineages