Investigação do teor de água no Biodiesel utilizado na composição do Diesel B comercializado por uma distribuidora de combustíveis em Manaus/AM / Investigation of water content in Biodiesel used in the composition of Diesel B marketed by a fuel distributor in Manaus/AM

O teor de água em biodiesel, segundo a ANP 45/2014, poderá apresentar valor de até 350 mg/kg no âmbito das distribuidoras de combustíveis. O biodiesel-B100, utilizado na distribuidora alvo deste estudo no estado do Amazonas, é proveniente de usinas localizadas no Estado de Mato Grosso. Até sua chegada em Manaus, ele percorre uma distância média de 2.514 km entre os dois estados. Sua logística durante o transporte é realizada em duas etapas, modal rodoviário por meio de caminhões-tanque (Lucas do Rio Verde/MT a Porto Velho/RO) e, em sequência, modal fluvial por meio de balsa-tanque (Porto Velho/RO a Manaus/AM). Após a realização de análises em pontos estratégicos neste percurso, um estudo sistemático de controle de qualidade do biodiesel com enfoque especial no teor de água por meio do ensaio de Karl Fisher, realizado no ano de 2015, demonstra que tais condições logísticas em associação com as características climáticas regionais amazônicas resultam na inobservância do atendimento à legislação ANP supracitada, isto é, a alta umidade encontrada pelo biodiesel durante seu percurso até Manaus somada a sua condição de alta higroscopicidade inviabiliza a permanência do teor de água deste produto dentro dos limites estabelecidos pela Agência Nacional do Petróleo, Gás Natural e Biocombustíveis

Perfil da qualidade do Biodiesel, Diesel B S10 e B S500 em uma distribuidora na Região Norte do Brasil / Quality profile of Biodiesel, Diesel B S10 and B S500 in a distributor in Northern Brazil

O Biodiesel é misturado ao diesel rodoviário, atualmente variando entre 10% e 12% de biodiesel, formando o diesel B S10 e Diesel B S500 que é distribuído para os postos revendedores e consumido em todo país. Esse percentual é responsável pela diminuição de enxofre emitido para atmosfera, pois o biodiesel não possui esse contaminante em sua composição, além de proporcionar efeitos de degradação biológica e físico-química extremamente danosos aos consumidores finais. O biodiesel utilizado na região norte é afetado pela logística, clima, tempo de armazenagem e pela própria higroscopicidade, agregando mais umidade do que outras regiões do país com a consequente elevação do teor de água acima do limite especificado de 350 mg/kg, regulado pela ANP n° 45/2014. Pretendeu-se com este trabalho traçar um perfil das análises físico-químicas dos produtos comercializado na região, bem como realizar um monitoramento sistemático do biodiesel e diesel B armazenados nos tanques de uma distribuidora da região norte. Face às misturas realizadas do biodiesel com o diesel fornecido entregue em Manaus buscou-se também traçar um do perfil da qualidade desses combustíveis nos tanques da distribuidora e que são comercializados para os postos de gasolina e consumidor final. Observando-se que biodiesel apresentou uma diminuição do teor de água mais ainda está acima do limite de especificação, o diesel B S10 está próximo do limite que atualmente é 200 mg/kg e o B S500 apresentou valor de teor de água abaixo de 300 mg/kg. 

Investigação de contaminação de combustíveis em rede de postos e a relação com as resoluções descumpridas: Investigation of fuel contamination in a gas station network and the relationship with non-compliant resolutions

Os postos revendedores são os agentes regulados pela ANP, que é quem realiza o contato final com os consumidores na entrega dos combustíveis. Os postos são responsáveis pelo recebimento do combustível, também pelo armazenamento final dos produtos gasolina comum, gasolina aditivada, diesel B S10, diesel B S500 e etanol hidratado combustível. Existem alguns fatores que causam impactos diretos na qualidade dos produtos, esses fatores estão diretamente ligados ao cometimento de infrações ou simplesmente descumprimento de legislações de qualidade, operacionais, manutenção ou de boas práticas. Esse trabalho verificou a relação entre a não observação dos descumprimentos das regulamentações que envolve o processo e as contaminações identificadas nos produtos de postos de gasolinas que foram visitados por equipe de qualidade nos anos 2019 a 2022. Essa avaliação considerou uma distribuidora que possui 25% de postos revendedores no Amazonas e foram considerados resoluções da ANP para essas avaliações e correlação

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

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

Treatment patterns and healthcare resource utilization for triple negative breast cancer in the Brazilian private healthcare system: a database study

Abstract In Brazil, data on the management of triple negative breast cancer (TNBC) as well as the burden of the disease in terms of health care resources utilization (HCRU) are scarce. To characterize the treatment patterns and HCRU associated with the management of Brazilian TNBC patients from the perspective of the private healthcare setting. Patients with at least one claim related to ICD-10 C50 from January 2012 until December 2017, and at least one claim for breast cancer treatment were assessed from a private claims database and classified as early and locally advanced, or metastatic. All patients with hormone and/or targeted therapy were excluded. Three thousand and four patients were identified, of which 82.8% were diagnosed in early and locally advanced stages. For early and locally advanced TNBC patients, 75.3% were treated in an adjuvant setting, mainly with anthracycline regimes. For mTNBC patients, bevacizumab regimens were the main treatment prescribed. More than 48% of mTNBC patients were switched to a second line of treatment. HCRU was higher for mTNBC patients when compared to early and locally advanced patients, with higher costs for metastatic disease management. The treatment setting has little influence on the HCRU pattern or the cost of disease management. The highest burden of disease was observed for metastatic management

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data