Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Structure and Dynamics of Heteroprotein Coacervates

International audienceUnder specific conditions, mixing two oppositely charged proteins induces liquid−liquid phase separation. The denser phase, or coacervate phase, can be potentially applied as a system to protect or encapsulate different bioactive molecules with a broad range of food and/or medical applications. The optimization of the design and efficiency of such systems requires a precise understanding of the structure and the equilibrium of the nanocomplexes formed within the coacervate. Here, we report on the nanocomplexes and the dynamics of the coacervates formed by two well-known, oppositely charged proteins β-lactoglobulin (β-LG, pI ≈ 5.2) and lactoferrin (LF, pI ≈ 8.5). Fluorescence recovery after photobleaching (FRAP) and solid-state nuclear magnetic resonance (NMR) experiments indicate the coexistence of several nanocomplexes as the primary units for the coacervation. To our knowledge, this is the first evidence of the occurrence of an equilibrium between quite unstable nanocomplexes in the coacervate phase. Combined with in silico docking experiments, these data support the fact that coacervation in the present heteroprotein system depends not only on the structural composition of the coacervates but also on the association rates of the proteins forming the nanocomplexes

Imaging and 3D morphological analysis of collagen fibrils

The recent booming of multiphoton imaging of collagen fibrils by means of second harmonic generation microscopy generates the need for the development and automation of quantitative methods for image analysis. Standard approaches sequentially analyse two-dimensional (2D) slices to gain knowledge on the spatial arrangement and dimension of the fibrils, whereas the reconstructed three-dimensional (3D) image yields better information about these characteristics. In this work, a 3D analysis method is proposed for second harmonic generation images of collagen fibrils, based on a recently developed 3D fibre quantification method. This analysis uses operators from mathematical morphology. The fibril structure is scanned with a directional distance transform. Inertia moments of the directional distances yield the main fibre orientation, corresponding to the main inertia axis. The collaboration of directional distances and fibre orientation delivers a geometrical estimate of the fibre radius. The results include local maps as well as global distribution of orientation and radius of the fibrils over the 3D image. They also bring a segmentation of the image into foreground and background, as well as a classification of the foreground pixels into the preferred orientations. This accurate determination of the spatial arrangement of the fibrils within a 3D data set will be most relevant in biomedical applications. It brings the possibility to monitor remodelling of collagen tissues upon a variety of injuries and to guide tissues engineering because biomimetic 3D organizations and density are requested for better integration of implants