Beneficial effects of physical activity in an HIV-infected woman with lipodystrophy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Lipodystrophy is common in patients infected with human immunodeficiency virus receiving highly active antiretroviral therapy, and presents with morphologic changes and metabolic alterations that are associated with depressive behavior and reduced quality of life. We examined the effects of exercise training on morphological changes, lipid profile and quality of life in a woman with human immunodeficiency virus presenting with lipodystrophy.</p> <p>Case presentation</p> <p>A 31-year-old Latin-American Caucasian woman infected with human immunodeficiency virus participated in a 12-week progressive resistance exercise training program with an aerobic component. Her weight, height, skinfold thickness, body circumferences, femur and humerus diameter, blood lipid profile, maximal oxygen uptake volume, exercise duration, strength and quality of life were assessed pre-exercise and post-exercise training. After 12 weeks, she exhibited reductions in her total subcutaneous fat (18.5%), central subcutaneous fat (21.0%), peripheral subcutaneous fat (10.7%), waist circumference (WC) (4.5%), triglycerides (9.9%), total cholesterol (12.0%) and low-density lipoprotein cholesterol (8.6%). She had increased body mass (4.6%), body mass index (4.37%), humerus and femur diameter (3.0% and 2.3%, respectively), high-density lipoprotein cholesterol (16.7%), maximal oxygen uptake volume (33.3%), exercise duration (37.5%) and strength (65.5%). Quality of life measures improved mainly for psychological and physical measures, independence and social relationships.</p> <p>Conclusions</p> <p>These findings suggest that supervised progressive resistance exercise training is a safe and effective treatment for evolving morphologic and metabolic disorders in adults infected with HIV receiving highly active antiretroviral therapy, and improves their quality of life.</p

Amazon Basin forest pyrogenic carbon stocks: First estimate of deep storage

Amazon Basin forest soils contain considerable soil organic carbon stocks; however, the contribution of soil pyrogenic carbon (PyC) to the total is unknown. PyC is derived from local fires (historical and modern) and external inputs via aeolian deposition. To establish an initial estimate of PyC stocks in non-terra preta forest with no known history of fire, to assess site and vertical variability, as well as to determine optimal sampling design, we sampled 37 one hectare forest plots in the Amazon Basin and analysed PyC via hydrogen pyrolysis of three individual samples per plot and of bulked samples to 200 cm depth. Using our data and published total organic carbon stocks, we present the first field-based estimate of total PyC stock for the Amazon Basin of 1.10 Pg over 0–30 cm soil depth, and 2.76 Pg over 0–100 cm soil depth. This is up to 20 times higher than previously assumed. Three individual samples per 1 ha are sufficient to capture the site variability of PyC in our plots. PyC showed significant, large-scale variability among plots. To capture 50% of the PyC in 200 cm soil profiles, soil must be sampled to a depth of at least 71 cm. PyC represents a significant (11%) portion of total organic carbon in soil profiles 0–200 cm depth. This finding highlights the potentially important role that historical fire has played in modifying soil C stocks. Our data suggest that PyC is an important carbon pool for long-term storage, involved in millennial scale biogeochemical cycling, particularly in the subsurface soil

Expanding tropical forest monitoring into Dry Forests: The DRYFLOR protocol for permanent plots

This is the final version. Available on open access from Wiley via the DOI in this recordSocietal Impact Statement Understanding of tropical forests has been revolutionized by monitoring in permanent plots. Data from global plot networks have transformed our knowledge of forests’ diversity, function, contribution to global biogeochemical cycles, and sensitivity to climate change. Monitoring has thus far been concentrated in rain forests. Despite increasing appreciation of their threatened status, biodiversity, and importance to the global carbon cycle, monitoring in tropical dry forests is still in its infancy. We provide a protocol for permanent monitoring plots in tropical dry forests. Expanding monitoring into dry biomes is critical for overcoming the linked challenges of climate change, land use change, and the biodiversity crisis.Newton FundNatural Environment Research Council (NERC)Fundação de Amparo à Pesquisa do Estado de São PauloCYTE

Evolutionary Heritage Influences Amazon Tree Ecology

Lineages tend to retain ecological characteristics of their ancestors through time. However, for some traits, selection during evolutionary history may have also played a role in determining trait values. To address the relative importance of these processes requires large-scale quantification of traits and evolutionary relationships among species. The Amazonian tree flora comprises a high diversity of angiosperm lineages and species with widely differing life-history characteristics, providing an excellent system to investigate the combined influences of evolutionary heritage and selection in determining trait variation. We used trait data related to the major axes of life-history variation among tropical trees (e.g. growth and mortality rates) from 577 inventory plots in closed-canopy forest, mapped onto a phylogenetic hypothesis spanning more than 300 genera including all major angiosperm clades to test for evolutionary constraints on traits. We found significant phylogenetic signal (PS) for all traits, consistent with evolutionarily related genera having more similar characteristics than expected by chance. Although there is also evidence for repeated evolution of pioneer and shade tolerant life-history strategies within independent lineages, the existence of significant PS allows clearer predictions of the links between evolutionary diversity, ecosystem function and the response of tropical forests to global change

Evolutionary Heritage Influences Amazon Tree Ecology

Lineages tend to retain ecological characteristics of their ancestors through time. However, for some traits, selection during evolutionary history may have also played a role in determining trait values. To address the relative importance of these processes requires large-scale quantification of traits and evolutionary relationships among species. The Amazonian tree flora comprises a high diversity of angiosperm lineages and species with widely differing life-history characteristics, providing an excellent system to investigate the combined influences of evolutionary heritage and selection in determining trait variation. We used trait data related to the major axes of life-history variation among tropical trees (e.g. growth and mortality rates) from 577 inventory plots in closed-canopy forest, mapped onto a phylogenetic hypothesis spanning more than 300 genera including all major angiosperm clades to test for evolutionary constraints on traits. We found significant phylogenetic signal (PS) for all traits, consistent with evolutionarily related genera having more similar characteristics than expected by chance. Although there is also evidence for repeated evolution of pioneer and shade tolerant life-history strategies within independent lineages, the existence of significant PS allows clearer predictions of the links between evolutionary diversity, ecosystem function and the response of tropical forests to global change

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