Glazed roman ceramic. A multi-analytical approach

A multi-analytical approach has been applied to characterize ancient glazed ceramics from the archaeological sites of Magna Mater temple and Domus Tiberiana on the Palatine Hill (Rome, Italy) dated between the 3rd and the early 5th century AD. The aim of this work is to investigate the production technologies of the ceramic body and the glazed coating and to explore the nature and the provenance of the raw materials. Optical microscopy (OM), Fourier transform infrared spectroscopy (FTIR) and X-ray powder diffraction (XRPD) results showed that the ceramic body is composed by quartz, K-feldspar and plagioclase, fragments of igneous and sedimentary rocks. The firing temperature was estimated at about 900-1000 °C, in uncontrolled atmosphere conditions. The mineralogical assemblage of the ceramic body is consistent with a local source of the raw materials. The results of electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDS) showed that the glazes contain different Si/ Pb ratios. In addition, X-ray fluorescence (XRF) detected the presence of Sn although its concentration does not allow defining the studied samples as tin-glazed ceramics. However, the occurrence of this element indicates an atypical Roman production, never recognized before in coeval samples from other archaeological sites

How microanalysis can be discriminant on black Pompeian wares

In the present work the advantages of punctual approaches are discussed in the discrimination of black wares from the Sanctuary of Venus Fisica (Pompeii, Italy), dated between the 2nd and 1st century BC. Black-gloss ware and "bucchero" samples are analyzed by a multi-analytical approach including optical microscopy (OM), X-ray powder diffraction (XRPD), scanning electron microscopy with Energy Dispersive Spectroscopy (SEM-EDS) and electron microprobe analysis (EMPA) to investigate the mineralogical and petrographic features of these artefacts. Grain size, firing conditions and potter’s expertise influenced the final appearance of the superficial decorative black layer. In addition, punctual chemical analysis was fundamental to verify the archaeological indication of specific production sites

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions

IntroductionCollaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.Areas coveredIn this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.Expert opinionStructure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost