Ticks as potential vectors of Mycobacterium leprae: Use of tick cell lines to culture the bacilli and generate transgenic strains.

Leprosy is an infectious disease caused by Mycobacterium leprae and frequently resulting in irreversible deformities and disabilities. Ticks play an important role in infectious disease transmission due to their low host specificity, worldwide distribution, and the biological ability to support transovarial transmission of a wide spectrum of pathogens, including viruses, bacteria and protozoa. To investigate a possible role for ticks as vectors of leprosy, we assessed transovarial transmission of M. leprae in artificially-fed adult female Amblyomma sculptum ticks, and infection and growth of M. leprae in tick cell lines. Our results revealed M. leprae RNA and antigens persisting in the midgut and present in the ovaries of adult female A. sculptum at least 2 days after oral infection, and present in their progeny (eggs and larvae), which demonstrates the occurrence of transovarial transmission of this pathogen. Infected tick larvae were able to inoculate viable bacilli during blood-feeding on a rabbit. Moreover, following inoculation with M. leprae, the Ixodes scapularis embryo-derived tick cell line IDE8 supported a detectable increase in the number of bacilli for at least 20 days, presenting a doubling time of approximately 12 days. As far as we know, this is the first in vitro cellular system able to promote growth of M. leprae. Finally, we successfully transformed a clinical M. leprae isolate by inserting the reporter plasmid pCHERRY3; transformed bacteria infected and grew in IDE8 cells over a 2-month period. Taken together, our data not only support the hypothesis that ticks may have the potential to act as a reservoir and/or vector of leprosy, but also suggest the feasibility of technological development of tick cell lines as a tool for large-scale production of M. leprae bacteria, as well as describing for the first time a method for their transformation

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

Insights into the role of CuO in the CO2 photoreduction process

Abstract The CO2 photoreduction process to produce light hydrocarbons is known to be influenced by the presence of CuO nanoparticles, but the actual role of this material, whether as a catalyst or a reactant, has not yet been revealed. In this work, we investigate the role of CuO nanoparticles produced by a solvothermal method as a catalyst in CO2-saturated water reaction media under UV light, considering the effects of different electrolytes (Na2C2O4, KBrO3, and NaOH) and temperatures on nanoparticle phase and activity. The electrolyte strongly influenced product selectivity (NaOH led to evolution of CH4, Na2C2O4 to CO, and KBrO3 to O2) and induced CuO phase change. A long-term analysis of these processes indicated that during the initial steps, CuO acted as a reactant, rather than as a catalyst, and was converted to CuCO3.Cu(OH)2, while the as-converted material acted as a catalyst in CO2 photoreduction, with conversion values comparable to those reported in the literature

CuO Decoration Controls Nb 2 O 5 Photocatalyst Selectivity in CO 2 Reduction

The reformation of CO2 through photocatalytic processes to obtain products with high energy value and compatibility with the current energy infrastructure is a compelling strategy to minimize the emission of CO2 into the atmosphere, one of the main greenhouse gases. However, practical application of such a photocatalytic system requires significant efforts for improved CO2 photoreduction performance and product selectivity. Thus, in the present work, CuO nanoparticles were combined with Nb2O5 in order to improve the photocatalytic properties of these semiconductors in the CO2 photoreduction process. Nb2O5/CuO heterojunctions were prepared via a solvothermal treatment method, while the experimental tools, such as FESEM, HRTEM, and DRS, were employed to evaluate the microstructural and electronic properties. We describe how CuO decoration over Nb2O5 adjusts its selectivity for CO2 reduction to CH4, HCOOH, or H3CCOOH in different contents. An investigation of CO2 photoreduction using different electron donors/scavengers (water, sodium oxalate, and potassium bromate) under ultraviolet radiation revealed that its decoration influences local CO production by modifying the selectivity. CO has been confirmed as the main intermediate for HCOOH and CH3COOH production, and CO2 reduction efficiency increases at low CuO content (2.5% wt), leading to the formation of soluble hydrocarbons, and increases for CH4 in higher amounts (10% wt)