Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

Purpose: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution.

Dicentric chromosome assay using a deep learning-based automated system.

The dicentric chromosome assay is the "gold standard" in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently developed automated system was evaluated for the dicentric chromosome assay.

Anesthesia in a Patient with Potocki-Lupski Syndrome.

Introduction: Potocki-Lupski syndrome (PTLS) is a rare disease caused by the duplication of a small segment of chromosome 17 (17p11.2). The clinical presentation of this syndrome is quite variable and includes hypotonia, failure to thrive, oropharyngeal dysphagia, developmental delay, and behavioral abnormalities.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial radiations. Dicentric chromosome assay (DCA) is one of the representative methods of dose estimation that discriminates the aberration in chromosomes modified by radiation. Despite the DCA-based automated radiation dose estimation methods proposed in previous studies, there are still limitations to the accuracy of dose estimation.

The influence of surrounding land cover on wetland habitat conditions: a case study of inland wetlands in South Korea.

Wetland ecosystems have been globally degraded and lost due to rapid urbanization and climate change. An assessment of national scale inventory, including wetland types and conditions, is urgently required to understand the big picture of endangered wetlands, such as where they are and how they look like. We analyzed the spatial patterns of each inland wetland type (brackish wetland was included) in South Korea and the relative importance of land cover categories on wetland conditions.