Global Daily Column Average CO at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning.

Accurate global carbon dioxide (CO) distribution with high spatial and temporal resolution is essential for understanding its dynamics and impacts on climate change. This study tackles the challenge of data gaps in satellite observations of greenhouse gases, caused by orbital and observational limitations. We reconstructed a comprehensive dataset of Column-averaged CO2 (XCO) concentrations by integrating re-analyzed data from the Copernicus Atmosphere Monitoring Service (CAMS) with observations from GOSAT and OCO-3 satellites.

Integrated genome-wide association and transcriptomic studies reveal genetic architecture of bulb storability of plentiful garlic germplasm resources.

Garlic is a widely utilized condiment and health product. However, garlic bulbs are prone to quality deterioration resulting in decrease of economic value during postharvest. In this study, the storability of 501 garlic accessions worldwide was evaluated based on the examination of decay index (DI), decay rate, sprouting rate, and bud-to-clove ratio in two consecutive years.

WDHD1 is over-expressed in nasopharyngeal carcinoma and may control the expression of ITGAV.

Nasopharyngeal carcinoma (NPC) is a highly metastatic and invasive malignant tumor that originates in the nasopharynx. The DNA-binding protein WD repeat and HMG-box DNA-binding protein 1 (WDHD1) are highly expressed in a variety of tumours, but its expression and mechanism of action in NPC have not been reported to date. To investigate the involvement of WDHD1 in NPC, we first mined databases for the gene expression profile of NPC.

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring.

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing.

[An object-oriented remote sensing image segmentation approach based on edge detection].

Satellite sensor technology endorsed better discrimination of various landscape objects. Image segmentation approaches to extracting conceptual objects and patterns hence have been explored and a wide variety of such algorithms abound. To this end, in order to effectively utilize edge and topological information in high resolution remote sensing imagery, an object-oriented algorithm combining edge detection and region merging is proposed.