Purpose: We have conducted for the first time a Malaysian postal dosimetry audit of external beam under non-reference conditions by evaluating the output performance while screening for systematic errors within the dosimetry chain. The potential use from the choice of detector were investigated along with the search for other sources of discrepancies.
Methods: Ten radiotherapy centres were audited, encompassing 16 megavoltage photon beam arrangements, adopting the IAEA postal dosimetry protocol for non-reference conditions, with a holder modified to accommodate three TLD types: Ge-doped cylindrical silica fibres (CF), Ge-doped flat silica fibres (FF), and TLD-100 powder.
Results: Eight of the centres operated within ± 5% of stated dose, one other exceeding tolerance for all measured points, and one did not return any dosimeters for analysis after failing the initial irradiations. Post remedial measures, the mean relative response for CF, FF, and TLD-100 was 1.00, 0.99, and 0.98 respectively, with associated coefficients of variation 6.87%, 6.45%, and 5.06%.
Conclusion: High quality radiotherapy clinical practice postal dosimetry audits that are based on sensitive TLDs are seen to be particularly effective in identifying and resolving dose delivery discrepancies.
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http://dx.doi.org/10.1016/j.ejmp.2022.06.011 | DOI Listing |
Due to the scattering and absorption of light, underwater images often exhibit degradation. Given the scarcity of paired real-world data and the inability of synthetic paired data to perfectly approximate real-world data, it's a challenge to restore these degraded images using deep neural networks. In this paper, a zero-shot underwater multi-scale image enhancement method (Zero-UMSIE) is proposed, which utilizes the isomorphism between the original underwater image and the re-degraded image.
View Article and Find Full Text PDFBMC Plant Biol
October 2024
Bioinformatics Group, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, 6708 PB, the Netherlands.
Background: Breeding of lettuce (Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions.
View Article and Find Full Text PDFProc SPIE Int Soc Opt Eng
February 2024
Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume.
View Article and Find Full Text PDFFront Plant Sci
March 2024
National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays.
View Article and Find Full Text PDFGenome Med
April 2024
Cancer Institute, Department of Oncology, Peking University Shenzhen Hospital, Shenzhen Peking University-the Hong Kong University of Science and Technology (PKU-HKUST) Medical Center; Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518000, China.
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