Ministério público na fronteira entre a justiça e a política

Analisa o processo de reconstrução institucional do Ministério Público brasileiro. Examina os principais textos legais que, nas últimas duas décadas, redefiníram as atividades do Ministério Público. Explica como a Constituição de 1988, ao consolidar normas anteriormente isoladas, produziu um arranjo institucional que introduziu uma extensa judicialização da politica e uma politização das instituições judiciais, especialmente do Ministério Público. Aborda a renovação doutrinal e ideológica dos promotores públicos. Discute a questão do combate à corrupção por meio do sistema judiciário. Aponta alguns dos limites e contradições da judicialização da política e da politização do Ministério Público no Brasil. Enfoca a questão da efetividade processual e o problema do foro privilegiado

Preventing misidentification of 25I-NBOH as 2C-I on routine GC-MS analyses

25I-NBOH is a novel psychoactive substance (NPS) recently reported to have been found on blotter paper samples seized on the streets of Brazil, and used as a replacement for the NBOMes now scheduled in many countries. The presence of this NPS on the street market may go undetected, because the most widely and routinely utilised analytical technique for drug sample analyses is gas chromatography-mass spectrometry (GC-MS), which can misidentify 25I-NBOH (and indeed the other members of the NBOH series), because of its degradation into 2C-I (and corresponding 2C for the other members of the series) within the injector, unless a derivatization procedure is employed, which is often non-standard. While direct detection of 25I-NBOH under routine GC-MS conditions is still achieved, a slight adjustment in the standard GC-MS method, including shortening of the solvent delay window, was found to enable the detection of an additional peak due to 25I-NBOH degradation. Consequently, the presence of this secondary early chromatographic peak allowed for the distinction between 25I-NBOH and 2C-I using routine GC-MS without resorting to derivatization (or other analytical processes), thus preventing misidentification of 25I-NBOH as 2C-I. © 2017 Japanese Association of Forensic Toxicology and Springer Japa

25I-NBOH: a new potent serotonin 5-HT2A receptor agonist identified in blotter paper seizures in Brazil

A new potent serotonin 5-HT2A receptor agonist was identified in blotter papers by several state level forensic laboratories in Brazil. 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, Fourier transform infrared and nuclear magnetic resonance analyses were performed to complete molecular characterization. Individual doses range from 300 - 1000 g. Despite its being a potent 5-HT2A receptor agonist, 25I-NBOH is neither registered in 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

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,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