6 research outputs found

    Live cell superresolution-SIM imaging analysis of the intercellular transport of microvesicles and costimulatory proteins via nanotubes between immune cells

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    Halász, Henriett1,+, Ghadaksaz, Ali Reza1,2,+, Madarász, Tamás1, Huber, Krisztina2, Harami, Gábor3, Tóth, Eszter Angéla2, Osteikoetxea-Molnár, Anikó2, Kovács, Mihály3, Balogi, Zsolt5, Nyitrai, Miklós1,4, Matkó, János2,*, Szabó-Meleg, Edin

    The potential of radiocarbon analysis for the detection of art forgeries

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    Art objects form an essential part of cultural heritage and are appreciated for their artistic values. However, the observed investment in art and capacity for high monetary returns encourages counterfeiting of art objects. The art market's lack of transparency and traditional confidential protocols amplifies the problem. Radiocarbon analysis provides a tool to detect anachronistic materials. Measurement of bomb peak radiocarbon, which was observed in the atmosphere during the last 70 years, can provide clear evidence of post-1950 material. Here we briefly introduce the method and discuss its application in detecting forgeries. Three accelerator mass spectrometry AMS laboratories performed a 14C dating inter-comparison study on the material used in art. Results obtained on modern cotton paper, two antique sheets of paper, one parchment, and one textile demonstrate the radiocarbon dating capacity to date the material accurately. The excellent agreement between laboratories is crucial for the broader application of this scientific tool in forensic studies and court cases.ISSN:0379-0738ISSN:1872-628

    Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

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    Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation induct i v e l y coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successf u l combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations wi l l lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by similar to 10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, sti l l in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIX E measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in tur n brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.Peer reviewe
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