Seaweed feed enhance the long-term recovery of bacterial community and carbon-nitrogen sequestration in eutrophic coastal wetland.

Seaweed feed offers a promising approach to enhance sustainability in aquaculture. While much research has focused on its effects on aquatic organisms, the impact of seaweed feed residuals on sediment carbon sequestration and bacterial community dynamics remains underexplored. This study aimed to address this gap through a 96-day incubation experiment using sediment from the coastal wetlands of Zhuhai in southern China.

Endocrine-Disrupting Chemical Exposure Induces Adverse Effects on the Population Dynamics of the Indo-Pacific Humpback Dolphin.

Cetaceans play a pivotal role in maintaining the ecological equilibrium of ocean ecosystems. However, their populations are under global threat from environmental contaminants. Various high levels of endocrine-disrupting chemicals (EDCs) have been detected in cetaceans from the South China Sea, such as the Indo-Pacific humpback dolphins in the Pearl River Estuary (PRE), suggesting potential health risks, while the impacts of endocrine disruptors on the dolphin population remain unclear.

Segmentation-guided multi-modal registration of liver images for dose estimation in SIRT.

Purpose: Selective internal radiation therapy (SIRT) requires a good liver registration of multi-modality images to obtain precise dose prediction and measurement. This study investigated the feasibility of liver registration of CT and MR images, guided by segmentation of the liver and its landmarks. The influence of the resulting lesion registration on dose estimation was evaluated.

Quantitative comparison of pre-treatment predictive and post-treatment measured dosimetry for selective internal radiation therapy using cone-beam CT for tumor and liver perfusion territory definition.

Background: Selective internal radiation therapy (SIRT) is a promising treatment for unresectable hepatic malignancies. Predictive dose calculation based on a simulation using Tc-labeled macro-aggregated albumin (Tc-MAA) before the treatment is considered as a potential tool for patient-specific treatment planning. Post-treatment dose measurement is mainly performed to confirm the planned absorbed dose to the tumor and non-tumor liver volumes.

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

Purpose: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.

Methods: A multi-scale CNN was modified for liver segmentation for SIRT patients.