Glucose is an important molecule for metabolism. It is more actively taken up in tumor cells than in normal cells, thus making it possible to detect the tumor. Radiolabeled glucose derivates have been successfully employed for tumor imaging for several decades. This review focuses on the development of various radiolabeled glucose derivatives as tumor imaging probes with single photon emission computed tomography (SPECT) and positron emission tomography (PET) and discusses basic research data, current status and future prospects of this class of imaging agents.

GlobeLand30, as one of the best Global Land Cover (GLC) product at 30-m resolution, has been widely used in many research fields. Due to the significant spectral confusion among different land cover types and limited textual information of Landsat data, the overall accuracy of GlobeLand30 is about 80 %. Although such accuracy is much higher than most other global land cover products, it cannot satisfy various applications. There is still a great need of an effective method to improve the quality of GlobeLand30. The explosive high-resolution satellite images and remarkable performance of Deep Learning on image classification provide a new opportunity to refine GlobeLand30. However, the performance of deep leaning depends on quality and quantity of training samples as well as model training strategy. Therefore, this paper 1) proposed an automatic training sample generation method via Google earth to build a large training sample set; and 2) explore the best training strategy for land cover classification using GoogleNet (Inception V3), one of the most widely used deep learning network. The result shows that the fine-tuning from first layer of Inception V3 using rough large sample set is the best strategy. The retrained network was then applied in one selected area from Xi’an city as a case study of GlobeLand30 refinement. The experiment results indicate that the proposed approach with Deep Learning and google earth imagery is a promising solution for further improving accuracy of GlobeLand30.

In this paper we use a newly compiled sample of ultra-compact structure in radio quasars and strong gravitational lensing systems with quasars acting as background sources to constrain six spatially flat and non-flat cosmological models (ΛCDM, PEDE, and DGP). These two sets of quasar data (time-delay measurements of six strong lensing systems and 120 intermediate-luminosity quasars calibrated as standard rulers) could break the degeneracy between the cosmological parameters (H 0 , Ω m , and Ω k ), and therefore provide more stringent cosmological constraints for the six cosmological models we study. A joint analysis of the quasar sample provides model-independent measurements of the Hubble constant H 0 , which are strongly consistent with that derived from the local distance ladder by the SH0ES collaboration in the ΛCDM and PEDE model. However, in the framework of the DGP cosmology (especially for a flat universe), the measured Hubble constant is in good agreement with that derived from the recent Planck 2018 results. In addition, our results show that zero spatial curvature is supported by the current lensed and unlensed quasar observations and that there is no significant deviation from a flat universe. For most of the cosmological models we study (flat ΛCDM, non-flat ΛCDM, flat PEDE, and non-flat PEDE), the derived matter density parameter is completely consistent with Ω m ∼ 0.30 in all the data sets, as expected based on the latest cosmological observations. Finally, according to the statistical deviance information criterion (DIC), the joint constraints provide substantial observational support to the flat PEDE model; however, they do not rule out dark energy being a cosmological constant and non-flat spatial hypersurfaces.