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

Transperitoneal laparoscopic pyeloplasty: Brazilian initial experience with 55 cases

PURPOSE: To evaluate prospectively the results obtained in 55 patients undergoing laparoscopic pyeloplasty through transperitoneal access. MATERIALS AND METHODS: From January 2005 to July 2009, fifty-five patients between 13 and 64 years old, were treated for ureteropelvic junction (UPJ) stenosis via a transperitoneal laparoscopy. All patients had clinical symptoms of high urinary obstruction and hydronephrosis confirmed by imaging methods. Anderson-Hynes dismembered pyeloplasty was performed in 51 patients and Fenger technique in the other 4 cases. Patients were clinically and imaging evaluated in the postoperative period at 3 and 6 months and then followed-up annually. RESULTS: The operative time ranged from 95 to 270 min. The mean hospital stay was 2 days. The average blood loss was 170 mL. The time to return to normal activities ranged from 10 to 28 days. Anomalous vessels were identified in 27 patients, intrinsic stenosis in 23 patients and 5 patients had high implantation of the ureter. Laparoscopic pyelolithotomy was successfully performed in 6 patients with associated renal stones. That series monitoring ranged from 1 to 55 months. One patient had longer urinary fistula (11 days), 3 patients had portal infection and 6 patients had prolonged ileus. There was one conversion due to technical difficulties. From the later postoperative complications, 2 patients had re-stenosis, one determined by Anderson-Hynes technique and the other by Fenger technique. The success rate was 95.65%. CONCLUSIONS: Laparoscopic pyeloplasty has functional results comparable to conventional open technique. It offers less morbidity, with aesthetic and post-operative convalescence benefits and lower complication rates