Efeito do peso no rendimento do processamento de pirarucu (Arapaima gigas): viabilidade comercial para a indústria frigorífica

Monografia apresentada ao Departamento de Engenharia de Pesca da Fundação Universidade Federal de Rondônia – UNIR, como parte dos requisitos para obtenção do título de Engenheira de Pesca. Orientadora: Prof.ª Dra. Jucilene CavaliO pirarucu (Arapaima gigas) apresenta desempenho zootécnico desejável para cultivo intensivo, além de possuir ótima aceitação comercial. O trabalho teve como objetivo analisar o rendimento de carcaça, manta e subprodutos do pirarucu, a fim de estabelecer padrões de peso economicamente viáveis para a comercialização da espécie. O estudo foi realizado no Frigorifico Zaltana Pescados, na região de Ariquemes, onde 99 exemplares de pirarucus cultivados em viveiros escavados, foram alocados em seis classes de peso (8 a 9,9 kg; 10 a 11,9 kg; 12 a 13,9kg; 14,0 a 19,9 kg; 20,0 a 27,9 kg e 28 a 43,9 kg). Apesar do rendimento de carcaça ser crescente, de 66 a 72% quanto maior a classe de peso, o rendimento em manta, produto comercializável, em relação ao peso de abate, apresentou rendimento médio de 49,7%, não diferindo entre as classes de peso (P>0,05). O rendimento de manta em relação ao peso da carcaça foi crescente (72 a 75%) quanto maiores as classes de peso. O rendimento em carcaça e em manta aumenta com o aumento do peso ao abate, contudo, abate de pirarucu a pesos acima de 28kg são inviáveis a indústria frigorífica em função do menor rendimento em produto comercializável devido a maior produção de resíduos. Apesar do rendimento de manta em relação ao abate não ter sido significativo, o lucro em relação ao peso manta foi crescente conforme melhores valores rendimentos de manta. O mesmo cenário se repetiu quando avaliado o retorno econômico em relação à comercialização de postas, porém com significância econômica inferior, onde classes de pesos IV e V proporcionam o melhor feedback; de R$1,01 a 1,04 a cada R$1,00 investido

Negative correlation between Placental Growth Factor and Endocan-1 in women with preeclampsia

Objective: To analyse Endocan-1, a biomarker of vascular endothelial related pathologies, and Placental growth factor (PlGF), an angiogenic factor and a placental dysfunction marker in patients with pre-eclampsia (PE). Methods: Case-control study conducted at São Lucas Hospital. Endocan-1 and PlGF levels were quantified in maternal plasma using MagPlexTH-C microspheres system and analysed by ANCOVA adjusted by BMI, gestational age and maternal age. To estimate the difference between groups, mean ratio (MR) and 95% confidence interval (CI) were calculated. Pearson correlation test was used to establish any association between Endocan-1 and PlGF levels. The null hypothesis was rejected when

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