Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Estimativa da área foliar de nabo forrageiro em função de dimensões foliares

O objetivo deste trabalho foi desenvolver um modelo para estimar a área foliar de nabo forrageiro (Raphanus sativus L. var. oleiferus Metzg) determinada por fotos digitais, em função do comprimento, ou da largura e/ou do produto comprimento vezes largura da folha. Aos 76 dias após a semeadura, foram coletadas 557 folhas da haste principal de 92 plantas, sendo mensurados o comprimento (C) e a largura (L) de cada folha, e calculado o produto comprimento × largura (C×L). Após, determinou-se a área foliar (Y), por meio do método de fotos digitais. Do total de folhas, separaram-se, aleatoriamente, 450 folhas para a construção de modelos do tipo quadrático, potência e linear de Y em função de C, da L, e/ou de C×L. 107 folhas foram usadas para a validação dos modelos. O modelo do tipo potência da área foliar obtida por meio do método de fotos digitais (Ŷ=0,6843x0,9221, R²=0,9862) em função do produto comprimento × largura é adequado para estimar a área foliar de nabo forrageiro

External Validation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score for Prediction of Functional Outcome After Subarachnoid Hemorrhage.

The Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus (HATCH) Score has previously shown to predict functional outcome in aneurysmal subarachnoid hemorrhage (aSAH). To validate the HATCH score. This is a pooled cohort study including prospective collected data on 761 patients with aSAH from 4 different hospitals. The HATCH score for prediction of functional outcome was validated using calibration and discrimination analysis (area under the curve). HATCH score model performance was compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score. At the follow-up of at least 6 months, favorable (Glasgow Outcome Score 4-5) and unfavorable functional outcomes (Glasgow Outcome Score 1-3) were observed in 512 (73%) and 189 (27%) patients, respectively. A higher HATCH score was associated with an increased risk of unfavorable outcome with a score of 1 showing a risk of 1.3% and a score of 12 yielding a risk of 67%. External validation showed a calibration intercept of -0.07 and slope of 0.60 with a Brier score of 0.157 indicating good model calibration and accuracy. With an area under the curve of 0.81 (95% CI 0.77-0.84), the HATCH score demonstrated superior discriminative ability to detect favorable outcome at follow-up compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score with 0.72 (95% CI 0.67-0.75) and 0.63 (95% CI 0.59-0.68), respectively. This multicenter external validation analysis confirms the HATCH score to be a strong independent predictor for functional outcome. Its incorporation into daily practice may be of benefit for goal-directed patient care in aSAH

The Barrow Neurological Institute Grading Scale as a Predictor for Delayed Cerebral Ischemia and Outcome After Aneurysmal Subarachnoid Hemorrhage: Data From a Nationwide Patient Registry (Swiss SOS).

The Barrow Neurological Institute (BNI) scale is a novel quantitative scale measuring maximal subarachnoid hemorrhage (SAH) thickness to predict delayed cerebral ischemia (DCI). This scale could replace the Fisher score, which was traditionally used for DCI prediction. To validate the BNI scale. All patient data were obtained from the prospective aneurysmal SAH multicenter registry. In 1321 patients, demographic data, BNI scale, DCI, and modified Rankin Scale (mRS) score up to the 1-yr follow-up (1FU) were available for descriptive and univariate statistics. Outcome was dichotomized in favorable (mRS 0-2) and unfavorable (mRS 3-6). Odds ratios (OR) for DCI of Fisher 3 patients (n = 1115, 84%) compared to a control cohort of Fisher grade 1, 2, and 4 patients (n = 206, 16%) were calculated for each BNI grade separately. Overall, 409 patients (31%) developed DCI with a high DCI rate in the Fisher 3 cohort (34%). With regard to the BNI scale, DCI rates went up progressively from 26% (BNI 2) to 38% (BNI 5) and corresponding OR for DCI increased from 1.9 (1.0-3.5, 95% confidence interval) to 3.4 (2.1-5.3), respectively. BNI grade 5 patients had high rates of unfavorable outcome with 75% at discharge and 58% at 1FU. Likelihood for unfavorable outcome was high in BNI grade 5 patients with OR 5.9 (3.9-8.9) at discharge and OR 6.6 (4.1-10.5) at 1FU. This multicenter external validation analysis confirms that patients with a higher BNI grade show a significantly higher risk for DCI; high BNI grade was a predictor for unfavorable outcome at discharge and 1FU