Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one hut with faster data training

Left atrial volume quantification using cardiac MRI in atrial fibrillation: comparison of the Simpson’s method with biplane area-length, ellipse, and three-dimensional methods

PURPOSELeft atrial volume is an important predictor of future arrhythmias, and it can be assessed by several different methods. Simpson’s method is well accepted as a reference standard, although no standardization exists for cardiac magnetic resonance (CMR). We aimed to compare the estimations of left atrial volumes obtained by the Simpson’s method with three other methods. MATERIALS AND METHODSEighty-one consecutive patients referred for CMR imaging between February 2007 and May 2010 were included in the study (47 males; mean age, 59.4±11.5 years; body mass index, 26.3±3.7 kg/m2). Left atrial volume measurements were performed using the Simpson’s, biplane area-length, ellipse, and three-dimensional methods. Results were correlated using a Bland-Altman plot and linear regression models and compared by two-tailed paired-sample t tests. Reader variability was also calculated. RESULTSLeft atrial volume measurements using the biplane area-length technique showed the best correlation with Simpson’s method (r=0.92; P 0.99). CONCLUSIONThe biplane area-length method can be used for left atrial volume measurement when the Simpson’s method cannot be performed. If these two methods are not feasible, then all methods are highly reproducible and can be used, but should not be used interchangeably for follow-up studies

Cytokine profile associated with human chronic schistosomiasis mansoni

This study objective was to evaluate the cytokines associated with early events of hepatic fibrosis in schistosomiasis mansoni. Hepatic fibrosis was classified by ultrasonography in 94 patients. Immunological evaluation was performed by measurement of secreted cytokines (interleukin IL-5, IL-10, IL-13, interferon-g, tumor necrosis factor-a and transforming growth factors-b) in peripherl blood mononuclear cells stimulated by Schistosoma mansoni antigens. Significantly, higher levels of IL-5, IL-10 and IL-13 were found in supernatants of SEA-stimulated PBMC from subjects with degree III hepatic fibrosis as compared to patients with degree I or II fibrosis, Significant increases in IL-5 and IL-13 levels were also observed in some of the subjects who remained untreated for one year following initial assessment and developed more serious fibrosis during this period. The data suggests a role for type 2 cytokines in early stages of hepatic fibrosis in human schistosomiasis mansoni

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

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

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