Jensby Ditlevsen (patchmonkey2)

Our study demonstrates pronounced horizon-dependent successional trajectories following wildfire and indicates interactive effects of time-since-fire, soil stoichiometry and spatial distance in the reassembly of belowground fungal communities in a cold and fire-prone region. This article is protected by copyright. All rights reserved.PURPOSE One technical barrier to patient-specific CT dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation. METHODS A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used The Lung CT Segmentation Challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, eac CONCLUSION This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency. This article is protected by copyright. All rights reserved.Epichloë festucae is an endophytic fungus that forms a symbiotic association with Lolium perenne. Here we analysed how the metabolome of the ryegrass apoplast changed upon infection of this host with sexual and asexual isolates of E. festucae. A metabolite fingerprinting approach was used to analyse the metabolite composition of apoplastic wash fluid from non-infected and infected L. perenne. Metabolites enriched or depleted in one or both of these treatments were identified using a set of interactive tools. A genetic approach in combination with tandem mass spectrometry was used to identify a novel product of a secondary metabolite gene cluster. Metabolites likely to be present in the apoplast were identified using MarVis in combination with the BioCyc and KEGG databases, and an in-house Epichloë metabolite database. We were able to identify the known endophyte-specific metabolites, peramine and epichloëcyclins, as well as a large number of unknown markers. 6-OHDA in vitro To determine whether these methods can be applied to the identification of novel Epichloë-derived metabolites, we deleted a gene encoding a NRPS (lgsA) that is highly expressed in planta. Comparative mass spectrometric analysis of apoplastic wash fluid from wild-type- versus mutant-infected plants identified a novel Leu/Ile glycoside metabolite present in the former. This article is protected by copyright. All rights reserved.PURPOSE Robust optimization is a computational expensive process resulting in long plan computation times. This issue is especially critical for moving targets as these cases need a large number of uncertainty scenarios to robustly optimize their treatment plans. In this study, we propose a novel worst-case robust optimization algorithm, called dynamic minimax, that accelerates the conventional minimax optimization. Dynamic minimax optimization aims at speeding up the plan optimization process by decreasing the number of evaluated scenarios in the optimization. METHODS For a given pool of scenarios (e.g., 63 = 7 setup  × 3 range  × 3 breathing phases), the proposed dynamic minimax algorithm only considers a reduced number of candidate-worst scenarios, selected from the full 63 scenario set. These scenarios are updated throughout the optimization by randomly sampling new scenar