A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images

The minimal change disease (MCD) and glomerulosclerosis (GS) are two common kidney diseases. Unless adequately treated, these diseases leads to chronic kidney diseases. Accurate differentiation of these two diseases is of paramount importance as their methods of treatment and prognoses are different. Thus, this article propose a method capable of differentiating MCD from GS in glomerulus biopsies images based on a new hybrid deep and texture feature space. We conducted an extensive study to determine the best set of features for image representation. Our feature extraction methodology, which includes Haraliks and geostatistics texture descriptors and pre-trained CNNs, resulted in 13,476 characteristics. We then used mutual information to order the elements by importance and select the best set for differentiating MCD from GS using the random forest classifier. The proposed method achieved an accuracy of 90.3% and a Kappa index of 80.5%. Representation of glomerulus biopsy images with a hybrid of deep and textural features facilitates the accurate differentiation of GS and MCD

Immune and inflammatory responses to Leishmania amazonensis isolated from different clinical forms of human leishmaniasis in CBA mice

Leishmania amazonensis causes different diseases depending on the host and parasitic virulence factors. In this study, CBA mice were infected with L. amazonensis isolates from patients with localized (Ba125), diffuse cutaneous (Ba276) or visceral leishmaniasis (Ba109). Mice infected with Ba125 and Ba276 progressed rapidly and lesions displayed an infiltrate rich in parasitized macrophages and were necrotic and ulcerated. Ba109 induced smaller lesions and a mixed inflammatory infiltrate without necrosis or ulceration. Ba109 induced an insidious disease with lower parasite load in CBA mice, similar to human disease. Levels of IFN-γ, IL-4 and IL-10 did not differ among the groups. Because all groups were unable to control the infection, expression of IL-4 associated with low production of IFN-γ in the early phase of infection may account for susceptibility, but others factors may contribute to the differences observed in inflammatory responses and infection progression. Evaluation of some parasitic virulence factors revealed that Ba276 exhibits higher ecto-ADPase and 5'-nucleotidase activities compared to the Ba109 and Ba125 strains. Both Ba276 and Ba125 had higher arginase activity in comparison to Ba109. Finally, these data suggest that the differences in enzyme activities among parasites can account for differences in host inflammatory responses and infection progression

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