Delta-like ligands (DLLs) control Notch signaling. DLL1, DLL3 and DLL4 are frequently deregulated in cancer and influence tumor growth, the tumor vasculature and tumor immunity, which play different roles in cancer progression. DLLs have attracted intense research interest as anti-cancer therapeutics. In this review, we discuss the role of DLLs in cancer and summarize the emerging DLL-relevant targeting methods to aid future studies.

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Over the last 5 decades, heart transplantation (HTx) has become the definitive gold standard surgical approach for patients with end-stage heart disease, such as heart failure. 1,2 However, even with immunosuppressive treatments, allograft rejection remains a major cause of morbidity and mortality because the pathogenesis, diagnosis and management of rejection remain highly undefined. 3,4 According to the International Society for Heart and Lung Transplantation (ISHLT) guidelines, HTx rejection can be divided into T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR), the diagnoses of which are based on the histology of the endomyocardial biopsies (EMBs). 5-9 Recently, molecular examination of EMBs has been proposed to improve the precision and accuracy of HTx rejection diagnosis. A new diagnostic system, the Molecular Microscope™ Diagnostic System

Unmanned aerial vehicle (UAV) remote sensing technology is gradually playing a role alternative to traditional field survey methods in monitoring plant functional traits of forest ecology. Few studies focused on monitoring functional trait ecology of underground forests of inaccessible negative terrain with UAV. The underground forests of tiankeng were discovered and are known as the inaccessible precious ecological refugia of extreme negative terrain. The aim of this research proposal is to explore the suitability of UAV technology for extracting the stand parameters of underground forests’ functional traits in karst tiankeng. Based on the multi-scale segmentation algorithm and object-oriented classification method, the canopy parameters (crown width and densities) of underground forests in degraded karst tiankeng were extracted by UAV remote sensing image data and appropriate features collection. First, a multi-scale segmentation algorithm was applied to attain the optimal segmentation scale to obtain the single wood canopy. Second, feature space optimization was used to construct the optimal feature space set for the image and then the k-nearest neighbor(k-NN) classifier was used to classify the image features. The features were classified into five types: canopy, grassland, road, gap, and bare land. Finally, both the crown densities and average crown width of the trees were calculated, and their accuracy were verified. The results showed that overall accuracy of object-oriented image feature classification was 85.60%, with 0.72 of kappa coefficient. The accuracy of tree canopy density extraction was 82.34%, for which kappa coefficient reached 0.91. The average canopy width of trees in the samples from the tiankeng-inside was 5.38 m, while that of the outside samples was 4.83 m. In conclusion, the canopy parameters in karst tiankeng were higher than those outside the tiankeng. Stand parameters extraction of karst tiankeng underground forests based on UAV remote sensing was relatively satisfactory. Thus, UAV technology provides a new approach to explore forest resources in inaccessible negative terrain such as karst tiankengs. In the future, we need to consider UAVs with more bands of cameras to extract more plant functional traits to promote the application of UAV for underground forest ecology research of more inaccessible negative terrain.