| 1. |
- Junker, Robert R., et al.
(författare)
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Covariation and phenotypic integration in chemical communication displays : Biosynthetic constraints and eco-evolutionary implications
- 2018
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Ingår i: New Phytologist. - : Wiley. - 0028-646X .- 1469-8137. ; 220:3, s. 739-749
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Tidskriftsartikel (refereegranskat)abstract
- Chemical communication is ubiquitous. The identification of conserved structural elements in visual and acoustic communication is well established, but comparable information on chemical communication displays (CCDs) is lacking. We assessed the phenotypic integration of CCDs in a meta-analysis to characterize patterns of covariation in CCDs and identified functional or biosynthetically constrained modules. Poorly integrated plant CCDs (i.e. low covariation between scent compounds) support the notion that plants often utilize one or few key compounds to repel antagonists or to attract pollinators and enemies of herbivores. Animal CCDs (mostly insect pheromones) were usually more integrated than those of plants (i.e. stronger covariation), suggesting that animals communicate via fixed proportions among compounds. Both plant and animal CCDs were composed of modules, which are groups of strongly covarying compounds. Biosynthetic similarity of compounds revealed biosynthetic constraints in the covariation patterns of plant CCDs. We provide a novel perspective on chemical communication and a basis for future investigations on structural properties of CCDs. This will facilitate identifying modules and biosynthetic constraints that may affect the outcome of selection and thus provide a predictive framework for evolutionary trajectories of CCDs in plants and animals.
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| 2. |
- Kehoe, Laura, et al.
(författare)
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Make EU trade with Brazil sustainable
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Kaushik, Sandeep S., et al.
(författare)
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Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network
- 2023
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Ingår i: Physics in Medicine and Biology. - : Institute of Physics (IOP). - 0031-9155 .- 1361-6560. ; 68:19
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Tidskriftsartikel (refereegranskat)abstract
- Objective: In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach: We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results: We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance: We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.
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