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

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

Oocyte development and ovarian maturation of the black triggerfish, Melichthys niger(Actinopterygii: Balistidae) in São Pedro e São Paulo Archipelago, Brazil

The oogenesis is a key stage in the reproductive development of an organism, which can be best understood from histological analysis of ovaries in different maturity stages. In order to provide information on the reproductive biology of the black triggerfish, M. niger, in particular on its oogenesis process, this study aimed at identifying and characterizing the oocyte development stages and its organization within the different stages of ovarian maturation based on specimens from São Pedro e São Paulo Archipelago. In this present report, a number of 294 ovaries were histologically analyzed. It was verified that they are composed of ovigerous lamellae containing oocytes at different development stages. Five different stages of oogenesis were identified: young cells, with an average size of 12.9 ìm; previtellogenic oocytes (perinucleolar), with an average size of 53.5 ìm; cortical-alveoli oocytes with an average size of 83.1 ìm; vitellogenic oocytes, with an average size of 160.4 ìm and mature oocytes, with an average size of 289.8 ìm. In addition to the germ cells, some somatic structures were also identified, such as: ovarian wall, follicular cells and blood vessels. Based on the type and number of oocytes observed, four stages of ovarian maturation were identified: early maturation, represented by only 2.2% of the sample; middle maturation, represented by 9.9%; mature, represented by 44.2% and resting, represented by 43.9%. The identification of five oocyte development stages in the ovarians from M. niger, suggested that the specie follows a pattern similar to that described for other marine fish

Progressive Resistance Exercise Training in CKD: A Feasibility Study

Skeletal muscle wasting in chronic kidney disease (CKD) is associated with morbidity and mortality. Resistance exercise results in muscle hypertrophy in the healthy population, but is underinvestigated in CKD. We aimed to determine the feasibility of delivering a supervised progressive resistance exercise program in CKD, with secondary aims to investigate effects on muscle size, strength, and physical functioning.info:eu-repo/semantics/publishedVersio

Progressive Resistance Exercise Training in CKD: A Feasibility Study

Background: Skeletal muscle wasting in chronic kidney disease (CKD) is associated with morbidity and mortality. Resistance exercise results in muscle hypertrophy in the healthy population, but is underinvestigated in CKD. We aimed to determine the feasibility of delivering a supervised progressive resistance exercise program in CKD, with secondary aims to investigate effects on muscle size, strength, and physical functioning. Study Design: Parallel randomized controlled feasibility study. Setting & Participants: Patients with CKD stages 3b to 4 were randomly assigned to the exercise (n = 20; 11 men; median age, 63 [IQR, 57-65] years; median estimated glomerular filtration rate, 28.5 [IQR, 19.0-32.0] mL/min/1.73 m[superscript: 2]) or nonexercise control (n = 18; 14 men; median age, 66 [IQR, 45-79] years; estimated glomerular filtration rate, 20.5 [IQR, 16.0-26.0] mL/min/1.73 m[superscript: 2]) group. Intervention: Patients in the exercise group undertook an 8-week progressive resistance exercise program consisting of 3 sets of 10 to 12 leg extensions at 70% of estimated 1-repetition maximum thrice weekly. Patients in the control group continued with usual physical activity. Outcomes: Primary outcomes were related to study feasibility: eligibility, recruitment, retention, and adherence rates. Secondary outcomes were muscle anatomical cross-sectional area, muscle volume, pennation angle, knee extensor strength, and exercise capacity. Measurements Two- and 3-dimensional ultrasonography of skeletal muscle, dynamometry, and shuttle walk tests at baseline and 8 weeks. Results: Of 2,349 patients screened, 403 were identified as eligible and 38 enrolled in the study. 33 (87%) completed the study, and those in the exercise group attended 92% of training sessions. No changes were seen in controls for any parameter. Progressive resistance exercise increased muscle anatomical cross-sectional area, muscle volume, knee extensor strength, and exercise capacity. Limitations: No blinded assessors, magnetic resonance imaging not used to assess muscle mass, lack of a healthy control group. Conclusions: This type of exercise is well tolerated by patients with CKD and confers important clinical benefits; however, low recruitment rates suggest that a supervised outpatient-based program is not the most practical implementation strategy