Many studies have revealed that SPX (SYG1/Pho81/XPR1) family genes play a key role in signal transduction related to phosphorus (P) deficiency in plants. Here, we identified 33 SPX gene family members in maize through genome-wide analysis and classified them into 4 subfamilies according to SPX structural characteristics (SPX, SPX-MFS, SPX-EXS and SPX-RING). The promoter regions of ZmSPXs are rich in biotic/abiotic-related stress elements. The quantitative real-time PCR analysis of 33 ZmSPXs revealed that all members except for ZmSPX3 of the SPX subfamily were significantly induced under P-deficient conditions, especially ZmSPX4.1 and ZmSPX4.2, which showed strong responses to low P stress and exhibited remarkably different expression patterns in low Pi sensitive and insensitive cultivars of maize. These results suggested that the SPX subfamily might play pivotal roles in P stress sensing and response. Experimental observations of subcellular localization in maize protoplasts indicated the following results, implying multiple roles in cell metabolism: ZmSPX2, ZmSPX5 and ZmSPX6 localized in the nucleus; ZmSPX1 and ZmSPX3 localized in the nucleus and cytoplasm; and ZmSPX4.2 localized in the chloroplast. A Y2H assay suggested that ZmPHR1 could interact with ZmSPX3, ZmSPX4.2, ZmSPX5, and ZmSPX6, indicating the involvement of these proteins in the P stress response in a ZmPHR1-mediated manner.

The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.

The precise localization of sensor nodes is a key link in underwater wireless sensor networks (UWSNs) applications. Furthermore, the nodes in UWSNs should be able to independently determine their relative positions before they begin to receive and transmit data to each other. Most of the existing underwater localization algorithms are based on the anchor nodes with known locations, and the rest of the nodes can be calculated by the anchor nodes. The iterative localization process has been proposed for the localization of UWSNs, in which some positioned nodes as reference nodes are selected for iterative localization. However, in real scenarios, some anchor nodes may be destroyed naturally or artificially damaged or even used to convey misleading information to interfere with the precise localization of other nodes underwater. This paper proposes a computationally efficient localization algorithm that can accurately locate the sensor nodes underwater to resist such network attacks. The proposed algorithm achieves better localization accuracy by iteratively removing misleading information transmitted from interfering nodes based on the Time Difference of Arrival ranging mapping and combining selective minimum gradient of AdaDelta gradient descent (AGD). The simulation results prove that the proposed algorithm performs better under network attacks than the existing algorithms.