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

T cell phenotypes in COVID-19 - a living review

COVID-19 is characterized by profound lymphopenia in the peripheral blood, and the remaining T cells display altered phenotypes, characterized by a spectrum of activation and exhaustion. However, antigen-specific T cell responses are emerging as a crucial mechanism for both clearance of the virus and as the most likely route to long-lasting immune memory that would protect against re-infection. Therefore, T cell responses are also of considerable interest in vaccine development. Furthermore, persistent alterations in T cell subset composition and function post-infection have important implications for patients’ long-term immune function. In this review, we examine T cell phenotypes, including those of innate T cells, in both peripheral blood and lungs, and consider how key markers of activation and exhaustion correlate with, and may be able to predict, disease severity. We focus on SARS-CoV-2-specific T cells to elucidate markers that may indicate formation of antigen-specific T cell memory. We also examine peripheral T cell phenotypes in recovery and the likelihood of long-lasting immune disruption. Finally, we discuss T cell phenotypes in the lung as important drivers of both virus clearance and tissue damage. As our knowledge of the adaptive immune response to COVID-19 rapidly evolves, it has become clear that while some areas of the T cell response have been investigated in some detail, others, such as the T cell response in children remain largely unexplored. Therefore, this review will also highlight areas where T cell phenotypes require urgent characterisation

The role and uses of antibodies in COVID-19 infections: a living review

Coronavirus disease 2019 has generated a rapidly evolving field of research, with the global scientific community striving for solutions to the current pandemic. Characterizing humoral responses towards SARS-CoV-2, as well as closely related strains, will help determine whether antibodies are central to infection control, and aid the design of therapeutics and vaccine candidates. This review outlines the major aspects of SARS-CoV-2-specific antibody research to date, with a focus on the various prophylactic and therapeutic uses of antibodies to alleviate disease in addition to the potential of cross-reactive therapies and the implications of long-term immunity

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Mastitis detection and prediction of milk composition using gas sensor and electrical conductivity

Milk is a complex raw material, and it requires a strict quality control. The analyses that milk undergoes upon its arrival to the dairy industry are essential for its quality control. However, some of these analyses are complex; expensive; and in some cases, subjective, such as the detection of sub-clinical mastitis. With that in mind, a portable device was developed with the objective to create a fast, accurate, and easy-to-use tool for the detection of mastitis and for the evaluation of the overall quality of milk. Samples of bovine raw milk were evaluated for acidity, composition, somatic cell count (SCC), and electrical conductivity, coupled with gas sensor MQ-135, responsible for detecting carbon dioxide and ammonium, and gas sensor MQ-3, responsible for detecting ethanol, benzene, methane, hexane, and carbon monoxide. Through the MQ-135 measurement, it was possible to determine the occurrence of mastitis milk with a 77% accuracy, while MQ-3 did not show promising results. Besides, it was also possible to estimate acidity, lactose, protein, ashes, and casein content with the use of our portable device, with a satisfactory level of accuracy when milk components were found within their normal range of variation. The sensor device developed shows potential to provide a fast decision-making tool in the dairy industry

Effect of cleaning treatment on adhesion of Streptococcus agalactiae to milking machine surfaces

Streptococcus agalactiae is a common subclinical mastitis agent in dairy cattle. In this study, the influence of hygiene procedure time on the adhesion of S. agalactiae to rubber and silicone surfaces used in milking machines was evaluated. The effect of hygiene practices on the thermodynamic and microscopic features of both surfaces was also evaluated. The hydrophobicity of milking machines and bacterial surfaces was investigated by measuring the contact angle with a goniometer 0, 30, 90, and 180 days after a full hygiene procedure simulated using rubber and silicone coupons. As the time of cleaning procedures enhanced, there was a reduction in the adhesion of S. agalactiae to both surfaces. The rubber and silicone surfaces were hydrophobic in all treatments (ΔGTOTsws0), which makes the adhesion process difficult. Photomicrographs showed rapid wear of both surfaces, pointing to damages caused by cleaning agents. However, the silicone was more resistant to cleaning and sanitizing treatments. Thus, this work shows that changes caused by hygiene procedures in the thermodynamic and in the morphology of milking surfaces have an enormous importance in the S. agalactiae adhesion and, consequently, in mastitis transmission between cattle herd

Thermodynamic and kinetic analyses of curcumin and bovine serum albumin binding

Bovine serum albumin (BSA)/curcumin binding and dye photodegradation stability were evaluated. BSA/curcumin complex showed 1:1 stoichiometry, but the thermodynamic binding parameters depended on the technique used and BSA conformation. The binding constant was of the order of 105 L·mol−1 by fluorescence and microcalorimetric, and 103 and 104 L·mol−1 by surface plasmon resonance (steady-state equilibrium and kinetic experiments, respectively). For native BSA/curcumin, fluorescence indicated an enthalpic and entropic driven process based on the standard enthalpy change (ΔH○F = −8.67 kJ·mol−1), while microcalorimetry showed an entropic driven binding process (ΔH○cal = 29.11 kJ·mol−1). For the unfolded BSA/curcumin complex, it was found thatp ΔH○F = −16.12 kJ·mol−1 and ΔH○cal = −42.63 kJ·mol−1. BSA (mainly native) increased the curcumin photodegradation stability. This work proved the importance of using different techniques to characterize the protein-ligand binding