In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

S-palmitoylation, catalyzed by a family of 23 zinc finger Asp-His-His-Cys (DHHC) domain-containing (ZDHHC) protein acyltransferases localized on the cell membrane. However, stemness genes modulated by ZDHHCs in lung adenocarcinoma (LUAD) remain to be defined. Previously, we have constructed a network of cancer stem cell genes, including INCENP, based on mRNA stemness indices (mRNAsi) of LUAD. INCENP has the function of a chromosomal passenger complex locating to centromeres, which is performed by the conserved region of its N-terminal domain. INCENP protein with a deletion of the first non-conserved 26 amino acid sequence failed to target centromeres. However, the exact function of the deleted sequence has not been elucidated. To identify novel cancer stem cell-relevant palmitoylated proteins and responsible ZDHHC enzymes in LUAD, we analyzed multi-omics data obtained from the database of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA). ZDHHC5 is distinguished from the ZDHHC family for being up-regulated in mRNA and protein levels and associated with malignant prognosis. ZDHHC5 was positively associated with INCENP, and the correlation score increased with LUAD stages. CSS-Palm results showed Cys¹⁵ was the S-palmitoylation site of INCENP. Interestingly, Cys¹⁵ locates in the 1–26 aa sequence of INCENP, and is a conserved site across species. As INCENP is a nuclear protein, we predicted that the nuclear localization signal of ZDHHC5 was specific to the importin αβ pathway, and the result of immunofluorescence proves that ZDHHC5 is located in the nucleoplasm, in addition to the plasma membrane. Therefore, our study indicates the S-palmitoylation of INCENP mediated by ZDHHC5 as a potential mechanism of S-palmitoylation to modulate CSCs in LUAD.

The benefits of intelligent reflecting surfaces (IRS) assisted massive multiple-input multiple-output systems are based on the accurate acquisition of channel state information but at the cost of the high pilot overhead. In this work, the sparse structure in angular domain is revealed, and then the uplink cascaded channel estimation is converted into a compressive sensing (CS) problem. However, the angle of arrival and angle of departure are fundamentally continuous values, and thus they usually do not fall precisely on the discrete grids, resulting in grid mismatch. In this case, the typical CS solution usually yields the compromised reconstruction performance. To address this issue, we proposed a deep learning-based approach with the traditional orthogonal matching pursuit followed by the residual network to improve the performance. Furthermore, a straightforward network structure is proposed to reduce computational complexity. Simulation results demonstrate that the proposed solutions achieve a better estimation performance and require lower pilot overhead compared with the state-of-the-art ones.

For conventional signaling, the length of the orthogonal pilot is required at least equal to the total number of user antennas. However, it is not recommended in the Internet of Things (IoT) due to the expensive cost paid in massive connectivities. Thanks to the sporadic nature of the massive connected users where a considerable fraction of users are inactive within a coherence time, the nonorthogonal pilot can be utilized with the joint channel estimation and active-user detection being modeled as a compressive sensing problem. According to the different antenna configuration methods employed by the base station, the constructed problems in this work are formulated into the single measurement vector and the multiple measurement vectors recovery problems. Also, we develop a model-driven deep learning algorithm to solve the problems based on the traditional alternative direction method of multipliers (ADMM) algorithm, where the iteration operation is unfolded into the network layer. The network parameters are learned with the help of the stochastic gradient descent algorithm. Simulation results show that the proposed approach can achieve better performance than an ADMM algorithm under the same computational complexity.

Orthogonal time frequency space (OTFS) is considered a promising modulation technology for integrated sensing and communication (ISAC) systems, which is robust against Doppler effects in time-frequency doubly-selective channels. However, limited by the communication bandwidth and frame duration, the fractional delay and Doppler problem in OTFS-ISAC systems, that is, the delay and Doppler of the channel are not integer multiples of the resolutions, leading to estimation errors. To help overcome this issue, in this work, we propose an amplitude barycenter calibration (ABC) algorithm, which uses observation points on the integer delay-Doppler grid to calibrate target estimates, thereby providing satisfactory sensing performance without broadening the bandwidth. In addition, the estimation error and Cramér–Rao lower bound of the ABC algorithm were derived by analyzing its sensing performance. The results demonstrated that the proposed ABC algorithm can improve distance and velocity sensing resolution while achieving acceptable communication performance.

Kidney renal clear cell carcinoma (KIRC) is the most common and fatal subtype of renal cancer. Antagonistic associations between selenium and cancer have been reported in previous studies. Selenium compounds, as anti-cancer agents, have been reported and approved for clinical trials. The main active form of selenium in selenoproteins is selenocysteine (Sec). The process of Sec biosynthesis and incorporation into selenoproteins plays a significant role in biological processes, including anti-carcinogenesis. However, a comprehensive selenoprotein mRNA analysis in KIRC remains absent. In the present study, we examined all 25 selenoproteins and identified key selenoproteins, glutathione peroxidase 3 (GPX3) and type 1 iodothyronine deiodinase (DIO1), with the associated prognostic biomarker leucine-rich repeat containing 19 (LRRC19) in clear cell renal cell carcinoma cases from The Cancer Genome Atlas (TCGA) database. We performed validations for the key gene expression levels by two individual clear cell renal cell carcinoma cohorts, GSE781 and GSE6344, datasets from the Gene Expression Omnibus (GEO) database. Multivariate survival analysis demonstrated that low expression of LRRC19 was an independent risk factor for OS. Gene set enrichment analysis (GSEA) identified tyrosine metabolism, metabolic pathways, peroxisome, and fatty acid degradation as differentially enriched with the high LRRC19 expression in KIRC cases, which are involved in selenium therapy of clear cell renal cell carcinoma. In conclusion, low expression of LRRC19 was identified as an independent risk factor, which will advance our understanding concerning the selenium adjuvant therapy of clear cell renal cell carcinoma.