A new potent serotonin 5-HT2A receptor agonist was identified in blotter papers by several state level forensic laboratories in Brazil. The 25I-NBOH is a labile molecule, which fragments into 2C-I when analyzed by routine seized material screening gas chromatography (GC) methods. GC–mass spectrometry (MS), liquid chromatography–quadrupole time-of-flight-MS, and Fourier transform infrared and nuclear magnetic resonance analyses were performed to complete molecular characterization. Individual doses range from 300 to 1000 μg. Despite its being a potent 5-HT2A receptor agonist, 25I-NBOH is neither registered in the United Nations Office on Drugs and Crime (UNODC) nor classified as a scheduled substance in most countries. Sweden and Brazil seem to be the only countries to control 25I-NBOH. To our knowledge, this is the first scientific report dealing with identification of 25I-NBOH in actual seizures.
Illicit substances found in blotter papers and tablets seized by police are traditionally identified and characterized from extracts of these materials. However, the procedures involved in extraction stages can result in artifacts and even contamination of the samples to be analyzed. On the other hand, high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) is a technique that requires no pretreatment steps, enabling direct analysis of the material, including the analysis of new illegal synthetic psychoactive substances. This study presents and discusses applications of the HR-MAS NMR in the analysis of tablets and blotter papers seized. Additional analysis in solution of the extracts of these materials was performed to compare the obtained spectral resolution signals. The results demonstrated that the HR-MAS NMR allowed the rapid identification of 3,4-methylenedioxy-N-methylcathinone (methylone), 4-methylmethcathinone (mephedrone), 2,5-dimethoxy-4-bromoamphetamine (DOB) and 2-(4-bromo-2,5-dimethoxyphenyl)-N-[(2-methoxyphenyl)methyl]ethanamine (25B-NBOMe) in samples of tablets and blotter papers seized in Goiás State, Brazil.
Text from titles and audio transcriptions, image thumbnails, number of likes, dislikes, and views are examples of available data in a YouTube video. Despite the variability, most standard Deep Learning models use only one type of data. Moreover, the simultaneous use of multiple data sources for such problems is still rare. To shed light on these problems, we empirically evaluate eight different multimodal fusion operations using embeddings extracted from image thumbnails and video titles of YouTube videos using standard Deep Learning models, ResNet-based SE-Net for image feature extraction, and BERT to NLP. Experimental results show that simple operations such as sum or subtract embeddings can improve the accuracy of models. The multimodal fusion operations in this dataset achieved 81.3% accuracy, outperforming the unimodal models by 3.86% (text) and 5.79% (video).
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