Search

In 2010-2018, Northern Patagonia featured the longest severe drought of the last millennium. This extreme dry spell triggered widespread growth decline and forest dieback. Nonetheless, the roles played by the two major mechanisms driving dieback, hydraulic failure and carbon starvation, are still not clear and understudied in this seasonally dry region. Here, for the 1800-2017 period, we apply a retrospective analysis of radial growth, wood anatomical traits (lumen area, cell-wall thickness) and δ13C and δ18O stable isotopes to assess dieback causes of the iconic conifer Araucaria araucana. We selected three stands where declining (defoliated) and nondeclining (not defoliated) trees coexisted along a precipitation gradient from the warm-dry Coastal Range to the cool-wet Andes. At all sites declining trees showed lower radial growth and lower theoretical hydraulic conductivity, suggesting a long-lasting process of hydraulic deterioration in their water transport system compared to nondeclining, coexisting trees. Wood anatomical traits evidenced that this divergence between declining and nondeclining trees started at least seven decades before canopy dieback. In the drier stands, declining trees showed higher water-use efficiency (WUE) throughout the whole period, which we attributed to early stomatal closure, suggesting a greater carbon starvation risk consistent with thinner cell walls. In the wettest stand, we found the opposite pattern. Here, a reduction in WUE coupled with thicker cell walls suggested increased carbon assimilation rates and exposure to drought-induced hydraulic failure. The δ18O values indicated different strategies of gas exchange between sites, which are likely a consequence of microsite conditions and water sources. Multiproxy, retrospective quantifications of xylem anatomical traits and tree-ring isotopes provide a robust tool to identify and forecast, which stands or trees will show dieback or, on the contrary, which will likely withstand and be more resilient to future hotter droughts. [ABSTRACT FROM AUTHOR]

Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inner wall of coronary arteries. This kind of medical imaging modalities is capable of providing detailed and significant information of lumen contour shape, which is very important for clinical diagnosis and analysis of cardiovascular diseases. Numerous learning based techniques have recently become very popular for coronary artery segmentation due to their impressive results. In this work, a supervised machine learning method for coronary artery lumen segmentation with high accuracy and minimal user interaction is designed. The fully discriminative lumen segmentation method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Additionally, the theoretical supports of the Gradient Boosting framework used in this work and its quadratic approximation are presented. The proposed algorithm is tested on the public datasets of boundary detection of lumen in IVUS challenge held in MICCAI 2011 and achieves a higher average Jaccard similarity of 96.8% and a lower mean error distance of 0.55 (in Cartesian coordinates), which shows higher accuracy compared to the existing learning based methods. Moreover, three real patient IVUS datasets are used to evaluate the performance of the proposed coronary artery lumen segmentation algorithm, which is shown to achieve lower percent error of lumen area of 1.861% ± 0.965%, 1.968% ± 0.864%, and 1.671% ± 0.584%, respectively, compared to the manually measured lumen area (ground truth). The proposed lumen segmentation method is found to be superior to the latest learning based segmentation techniques. Given the efficiency and robustness, our method has great potential in IVUS images processing and coronary artery segmentation and quantification. Novelty Statement: The main contributions are summarized in the following aspects:A detailed review of related work about learning based coronary artery lumen segmentation in intravascular ultrasound images is presented.A fully discriminative lumen segmentation method jointly learning a classifier our weak learners rely on and the features of the classifier is developed.The theoretical supports of the Gradient Boosting framework and its quadratic approximation used in this work are presented. [ABSTRACT FROM AUTHOR]

The effects of climate change on high‐latitude forest ecosystems are complex, making forecasts of future scenarios uncertain. The predicted lengthening of the growing season under warming conditions is expected to increase tree growth rates. However, there is evidence of an increasing sensitivity of the boreal forest to drought stress. To assess the influence of temperature and precipitation on the growth of black spruce (Picea mariana), we investigated long‐term series of wood anatomical traits on 20 trees from four sites along 600 km, the latitudinal range of the closed boreal forest in Quebec, Canada. We correlated the anatomical traits resolved at intraring level with daily temperature, vapor pressure deficit (VPD), and precipitation during the 1943–2010 period. Tree‐ring width, number of cells per ring and cell wall thickness were positively affected by spring and summer daily mean and maximum temperature at the northern sites. These results agree with the well‐known positive effect of high temperatures on tree ring formation at high latitudes. However, we captured, for the first time in this region, the latent impact of water availability on xylem traits. Indeed, in all the four sites, cell lumen area showed positive correlations with daily precipitation (mostly at low latitude), and/or negative correlations with daily mean and maximum temperature and VPD (mostly at high latitude). We inferred that drought, due to high temperatures, low precipitations, or both, negatively affects cell enlargement across the closed boreal forest, including the northernmost sites. The production of tracheids with narrower lumen, potentially more resistant to cavitation, could increase xylem hydraulic safety under a warmer and drier climate. However, this would result in lower xylem conductivity, with consequent long‐term hydraulic deterioration, growth decline, and possibly lead to tree dieback, as observed in other forest ecosystems at lower latitudes. [ABSTRACT FROM AUTHOR]