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

Isolation, expansion and differentiation of mesenchymal stromal cells from rabbits' bone marrow

Abstract: Tissue engineering has been a fundamental technique in the regenerative medicine field, once it permits to build tri-dimensional tissue constructs associating undifferentiated mesenchymal cells (or mesenchymal stromal cells - MSCs) and scaffolds in vitro. Therefore, many studies have been carried out using these cells from different animal species, and rabbits are often used as animal model for in vivo tissue repair studies. However, most of the information available about MSCs harvesting and characterization is about human and murine cells, which brings some doubts to researchers who desire to work with a rabbit model in tissue repair studies based on MSCs. In this context, this study aimed to add and improve the information available in the scientific literature providing a complete technique for isolation, expansion and differentiation of MSCs from rabbits. Bone marrow mononuclear cells (BMMCs) from humerus and femur of rabbits were obtained and to evaluate their proliferation rate, three different culture media were tested, here referred as DMEM-P, DMEM´S and α-MEM. The BMMCs were also cultured in osteogenic, chondrogenic and adipogenic induction media to prove their multipotentiality. It was concluded that the techniques suggested in this study can provide a guideline to harvest and isolate MSCs from bone marrow of rabbits in enough amount to allow their expansion and, based on the laboratory experience where the study was developed, it is also suggested a culture media formulation to provide a better cell proliferation rate with multipotentiality preservation