Metabolites that confirm induction and release of dormancy phases in sweet cherry buds

Here we report on metabolites found in a targeted profiling of ‘Summit’ flower buds for nine years, which could be indicators for the timing of endodormancy release (t1) and beginning of ontogenetic development (t1*). Investigated metabolites included chrysin, arabonic acid, pentose acid, sucrose, abscisic acid (ABA), and abscisic acid glucose ester (ABA-GE). Chrysin and water content showed an almost parallel course between leaf fall and t1*. After ‘swollen bud’, water content raised from ~60 to ~80% at open cluster, while chrysin content decreased and lost its function as an acetylcholinesterase inhibitor. Both parameters can be suitable indicators for t1*. Arabonic acid showed a clear increase after t1*. Pentose acid would be a suitable metabolite to identify t1 and t1*, but would not allow describing the ecodormancy phase, because of its continuously low value during this time. Sucrose reached a maximum during ecodormancy and showed a significant correlation with air temperature, which confirms its cryoprotective role in this phase. The ABA content showed maximum values during endodormancy and decreased during ecodormancy, reaching 50% of its content t1 at t1*. It appears to be the key metabolite to define the ecodormancy phase. The ABA-GE was present at all stages and phases and was much higher than the ABA content and is a readily available storage pool in cherry buds.Peer Reviewe

Metabolites in Cherry Buds to Detect Winter Dormancy

Winter dormancy is still a “black box” in phenological models, because it evades simple observation. This study presents the first step in the identification of suitable metabolites which could indicate the timing and length of dormancy phases for the sweet cherry cultivar ‘Summit’. Global metabolite profiling detected 445 named metabolites in flower buds, which can be assigned to different substance groups such as amino acids, carbohydrates, phytohormones, lipids, nucleotides, peptides and some secondary metabolites. During the phases of endo- and ecodormancy, the energy metabolism in the form of glycolysis and the tricarboxylic acid (TCA) cycle was shut down to a minimum. However, the beginning of ontogenetic development was closely related to the up-regulation of the carbohydrate metabolism and thus to the generation of energy for the growth and development of the sweet cherry buds. From the 445 metabolites found in cherry buds, seven were selected which could be suitable markers for the ecodormancy phase, whose duration is limited by the date of endodormancy release (t1) and the beginning of ontogenetic development (t1*). With the exception of abscisic acid (ABA), which has been proven to control bud dormancy, all of these metabolites show nearly constant intensity during this phase.Peer Reviewe

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe