The eleven-nineteen lysine-rich leukemia (ELL) gene undergoes translocation and fuses in-frame to the multiple lineage leukemia gene in a substantial proportion of patients suffering from acute forms of leukemia. Studies show that ELL indirectly modulates transcription by serving as a regulator for transcriptional elongation as well as for p53, U19/Eaf2, and steroid receptor activities. Our in vitro and in vivo data demonstrate that ELL could also serve as a transcriptional factor to directly induce transcription of the thrombospondin-1 (TSP-1) gene. Experiments using ELL deletion mutants established that full-length ELL is required for the TSP-1 up-regulation and that the transactivation domain likely resides in the carboxyl terminus. Moreover, the DNA binding domain may localize to the first 45 amino acids of ELL. Not surprisingly, multiple lineage leukemia-ELL, which lacks these amino acids, did not induce expression from the TSP-1 promoter. In addition, the ELL core-response element appears to localize in the ؊1426 to ؊1418 region of the TSP-1 promoter. Finally, studies using zebrafish confirmed that ELL regulates TSP-1 mRNA expression in vivo, and ELL could inhibit zebrafish vasculogenesis, at least in part, through up-regulating TSP-1. Given the importance of TSP-1 as an anti-angiogenic protein, our findings may have important ramifications for better understanding cancer. ELL4 was first identified in acute myeloid leukemia as a translocation partner of MLL (1). Wild-type MLL positively regulates expression of Hox genes, important in embryonic development and hematopoiesis (2-4), but the formation of MLL chimeras dysregulates this expression, contributing to leukemogenesis (4 -14). MLL partners also likely contribute to leukemogenesis. One such partner is ELL, which fuses with MLL as a result of the frequent translocation event t(11;19) (q23; p13.1) that occurs in acute myeloid leukemia (1). In vitro studies showed that wild-type ELL can increase the rate of polymerase II transcriptional elongation by suppressing transient pausing (15,16), an activity shared by the two paralogs of ELL found in mammalian cells, ELL2 and ELL3 (17,18). The targeted knockout of ELL in mice caused embryonic lethality, suggesting an essential role for this protein in early embryonic development (19). Further functional analyses revealed that ELL regulates cell growth and survival (20) and serves as a selective co-regulator for steroid receptor functions (21). In all, these studies point to the importance of ELL in normally functioning cells.Like other MLL translocation products, the chimera MLL-ELL appears to play an important role in leukemogenesis (22,23). The MLL-ELL fusion product contains the amino-terminal region of MLL fused to amino acids 46 -621 of ELL that include the elongation domain and a lysine-rich region (1). Transplantation of blood progenitor cells transduced with MLL-ELL into sublethally irradiated normal mice resulted in morphological and clinical disease manifestations closely resembling those observed in pa...

Insect identification is the basis of insect research and disaster control and is of great importance for the design of pest control strategies and the protection of beneficial insects. Due to human subjective limitations and the small size and uneven distribution of pests, traditional methods of distinguishing and counting pest types based on experience cannot quickly and accurately detect and identify pests. Therefore, this paper proposes an object detection algorithm based on the improved Mask R-CNN model, aiming to improve the accuracy and efficiency in pest identification and counting. The algorithm improves the FPN structure in the feature extraction network and increases the weight coefficient when fusing feature layers of different scales. Based on the task of target detection and recognition, weight coefficient is adjusted to a proper parameter so that the semantic information and positioning information can be made full use to achieve more accurate recognition and positioning. The results of the experimental analysis of 1000 sample images show that the improved Mask R-CNN model has a recognition and detection accuracy of 99.4%, which is 2.7% higher than that of the unimproved Mask R-CNN model. The main contribution of this method is to improve the detection speed, and at the same time, the recognition accuracy has been significantly improved. This algorithm provides technical support for pest detection in the agricultural field and makes a contribution to the intellectualization of agricultural management.