Identification of candidate genes and clarification of the maintenance of the green pericarp of weedy rice grains

The weedy rice (Oryza sativa f. spontanea) pericarp has diverse colors (e.g., purple, red, light-red, and white). However, research on pericarp colors has focused on red and purple, but not green. Unlike many other common weedy rice resources, LM8 has a green pericarp at maturity. In this study, the coloration of the LM8 pericarp was evaluated at the cellular and genetic levels. First, an examination of their ultrastructure indicated that LM8 chloroplasts were normal regarding plastid development and they contained many plastoglobules from the early immature stage to maturity. Analyses of transcriptome profiles and differentially expressed genes revealed that most chlorophyll (Chl) degradation-related genes in LM8 were expressed at lower levels than Chl a/b cycle-related genes in mature pericarps, suggesting that the green LM8 pericarp was associated with inhibited Chl degradation in intact chloroplasts. Second, the F2 generation derived from a cross between LM8 (green pericarp) and SLG (white pericarp) had a pericarp color segregation ratio of 9:3:4 (green:brown:white). The bulked segregant analysis of the F2 populations resulted in the identification of 12 known genes in the chromosome 3 and 4 hotspot regions as candidate genes related to Chl metabolism in the rice pericarp. The RNA-seq and sqRT-PCR assays indicated that the expression of the Chl a/b cycle-related structural gene DVR (encoding divinyl reductase) was sharply up-regulated. Moreover, genes encoding magnesium-chelatase subunit D and the light-harvesting Chl a/b-binding protein were transcriptionally active in the fully ripened dry pericarp. Regarding the ethylene signal transduction pathway, the CTR (encoding an ethylene-responsive protein kinase) and ERF (encoding an ethylene-responsive factor) genes expression profiles were determined. The findings of this study highlight the regulatory roles of Chl biosynthesis- and degradation-related genes influencing Chl accumulation during the maturation of the LM8 pericarp

Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information

The normal communication between smart meter and concentrator is a key factor influencing the normal function of users’ power consumption systems. To solve the communication failure of the smart meter caused by the signal conflict as well as the collected consecutive information abnormality from the same smart meter, according to the chain optimization index, the networking method of static and dynamic combination proposed in this paper is first used to picked out the optimal relay for a smart meter belonging to multiple relay communication ranges. Meanwhile, the communication with other secondary relays is closed to avoid signal conflict. Then the paper forms different combinations of collected data and these combinations are trained in the extreme learning machine (ELM) to find the characteristics value of power consumption information. Finally, in MATLAB simulation, if ELM detects the abnormal information, new communication path could be promptly found through dynamic adjustment of chain optimization weighted coefficient and the weighted coefficient of the number of the relayed smart meters. It solves the problem of consecutive information abnormality from the same smart meter and raises the reliability of smart meter’s communication, having a significantly meaning to guarantee the normal function of users’ power consumption system

An improved U-Net-based in situ root system phenotype segmentation method for plants

The condition of plant root systems plays an important role in plant growth and development. The Minirhizotron method is an important tool to detect the dynamic growth and development of plant root systems. Currently, most researchers use manual methods or software to segment the root system for analysis and study. This method is time-consuming and requires a high level of operation. The complex background and variable environment in soils make traditional automated root system segmentation methods difficult to implement. Inspired by deep learning in medical imaging, which is used to segment pathological regions to help determine diseases, we propose a deep learning method for the root segmentation task. U-Net is chosen as the basis, and the encoder layer is replaced by the ResNet Block, which can reduce the training volume of the model and improve the feature utilization capability; the PSA module is added to the up-sampling part of U-Net to improve the segmentation accuracy of the object through multi-scale features and attention fusion; a new loss function is used to avoid the extreme imbalance and data imbalance problems of backgrounds such as root system and soil. After experimental comparison and analysis, the improved network demonstrates better performance. In the test set of the peanut root segmentation task, a pixel accuracy of 0.9917 and Intersection Over Union of 0.9548 were achieved, with an F1-score of 95.10. Finally, we used the Transfer Learning approach to conduct segmentation experiments on the corn in situ root system dataset. The experiments show that the improved network has a good learning effect and transferability

Annexin A1 can inhibit the in vitro invasive ability of nasopharyngeal carcinoma cells possibly through Annexin A1/S100A9/Vimentin interaction.

Annexin A1 is a member of a large superfamily of glucocorticoid-regulated, calcium- and phospholipid-binding proteins. Our previous studies have shown that the abnormal expression of Annexin A1 is related to the occurrence and development of nasopharyngeal carcinoma (NPC). To understand the roles of Annexin A1 in the tumorigenesis of NPC, targeted proteomic analysis was performed on Annexin A1-associated proteins from NPC cells. We identified 436 proteins associated with Annexin A1, as well as two Annexin A1-interacted key proteins, S100A9 and Vimentin, which were confirmed by co-immunoprecipitation. Gene function classification revealed that the Annexin A1-associated proteins can be grouped into 21 clusters based on their molecular functions. Protein-protein interaction analysis indicated that Annexin A1 /S100A9/Vimentin interactions may be involved in the invasion and metastasis of NPC because they can form complexes in NPC cells. The down-regulation of Annexin A1 in NPC may lead to the overexpression of S100A9/Vimentin, which may increase the possibility of the invasion ability of NPC cells by adjusting the function of cytoskeleton proteins. Results suggested that the biological functions of Annexin A1 in NPC were diverse, and that Annexin A1 can inhibit the in vitro invasive ability of NPC cells through Annexin A1 /S100A9/Vimentin interaction

CC Chemokine Ligand-2: A Promising Target for Overcoming Anticancer Drug Resistance

CC chemokine ligand-2 (CCL2), a proinflammatory chemokine that mediates chemotaxis of multiple immune cells, plays a crucial role in the tumor microenvironment (TME) and promotes tumorigenesis and development. Recently, accumulating evidence has indicated that CCL2 contributes to the development of drug resistance to a broad spectrum of anticancer agents, including chemotherapy, hormone therapy, targeted therapy, and immunotherapy. It has been reported that CCL2 can reduce tumor sensitivity to drugs by inhibiting drug-induced apoptosis, antiangiogenesis, and antitumor immunity. In this review, we mainly focus on elucidating the relationship between CCL2 and resistance as well as the underlying mechanisms. A comprehensive understanding of the role and mechanism of CCL2 in anticancer drug resistance may provide new therapeutic targets for reversing cancer resistance