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

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

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

In Acute Stroke, Can CT Perfusion-Derived Cerebral Blood Volume Maps Substitute for Diffusion-Weighted Imaging in Identifying the Ischemic Core?

Background and Purpose In the treatment of patients with suspected acute ischemic stroke, increasing evidence suggests the importance of measuring the volume of the irreversibly injured “ischemic core.” The gold standard method for doing this in the clinical setting is diffusion-weighted magnetic resonance imaging (DWI), but many authors suggest that maps of regional cerebral blood volume (CBV) derived from computed tomography perfusion imaging (CTP) can substitute for DWI. We sought to determine whether DWI and CTP-derived CBV maps are equivalent in measuring core volume. Methods: 58 patients with suspected stroke underwent CTP and DWI within 6 hours of symptom onset. We measured low-CBV lesion volumes using three methods: “objective absolute,” i.e. the volume of tissue with CBV below each of six published absolute thresholds (0.9–2.5 mL/100 g), “objective relative,” whose six thresholds (51%-60%) were fractions of mean contralateral CBV, and “subjective,” in which two radiologists (R1, R2) outlined lesions subjectively. We assessed the sensitivity and specificity of each method, threshold, and radiologist in detecting infarction, and the degree to which each over- or underestimated the DWI core volume. Additionally, in the subset of 32 patients for whom follow-up CT or MRI was available, we measured the proportion of CBV- or DWI-defined core lesions that exceeded the follow-up infarct volume, and the maximum amount by which this occurred. Results: DWI was positive in 72% (42/58) of patients. CBV maps’ sensitivity/specificity in identifying DWI-positive patients were 100%/0% for both objective methods with all thresholds, 43%/94% for R1, and 83%/44% for R2. Mean core overestimation was 156–699 mL for objective absolute thresholds, and 127–200 mL for objective relative thresholds. For R1 and R2, respectively, mean±SD subjective overestimation were -11±26 mL and -11±23 mL, but subjective volumes differed from DWI volumes by up to 117 and 124 mL in individual patients. Inter-rater agreement regarding the presence of infarction on CBV maps was poor (kappa = 0.21). Core lesions defined by the six objective absolute CBV thresholds exceeded follow-up infarct volumes for 81%-100% of patients, by up to 430–1002 mL. Core estimates produced by objective relative thresholds exceeded follow-up volumes in 91% of patients, by up to 210-280 mL. Subjective lesions defined by R1 and R2 exceeded follow-up volumes in 18% and 26% of cases, by up to 71 and 15 mL, respectively. Only 1 of 23 DWI lesions (4%) exceeded final infarct volume, by 3 mL. Conclusion: CTP-derived CBV maps cannot reliably substitute for DWI in measuring core volume, or even establish which patients have DWI lesions

Objective measurement of CBV lesion volume.

<p>The six smaller images depict low-CBV lesions that were delineated automatically, in a single slice of one patient’s CBV maps, by rendering in white all brain pixels in which CBV was below each of six different thresholds. Each threshold is listed below its corresponding image, along with the total volume of tissue with CBV below that threshold in all slices. For clarity, the lesions identified by only three of the six absolute thresholds are shown in the top row, and lesions identified by only three of the six relative thresholds are shown in the bottom row. The larger image on the right is a DWI image obtained from the same patient, at approximately the same level. The gold standard core volume, measured over all DWI slices, was 117 mL.</p