Circular RNAs (circRNAs) are a class of noncoding RNAs that are broadly expressed in various biological cells and function in regulating gene expression. However, the molecular mechanisms that link circRNAs with progression of papillary thyroid carcinoma (PTC) are not well understood. In the present study, the function of circ_0006156 (circFNDC3B) was investigated in human PTC cells. First, we detected the expression of circFNDC3B in PTC tissues and PTC cell lines by RT-PCR. A luciferase reporter assay and AGO2-RNA immunoprecipitation (RIP) was used to confirm the relationship between circFNDC3B and microRNA (miR)-1178. PTC cells were stably transfected with small interfering RNA (siRNA) against circFNDC3B, and cell proliferation, migration, and invasion were detected to evaluate the effect of circFNDC3B in PTC, while tumorigenesis was assayed in nude mice. In this study, circFNDC3B was observed to be upregulated in PTC tissues and cell lines. Knockdown of circFNDC3B inhibited cell proliferation and promoted cell apoptosis in PTC cells. Bioinformatics analysis predicted that there is a circFNDC3B/miR-1178/Toll-like receptor 4 (TLR4) axis in PTC. The dual-luciferase reporter system validated the direct interaction of circFNDC3B, miR-1178, and TLR4. Furthermore, circFNDC3B facilitates PTC progression in vivo. Importantly, we demonstrated that circFNDC3B was upregulated in serum exosomes from PTC patients. In summary, our study demonstrated that circFNDC3B modulates PTC progression through the miR-1178/TLR4 pathway. Our findings indicated that circFNDC3B may serve as a promising therapeutic target for the treatment of PTC patients.

Circular RNAs (circRNAs) are a class of non-coding RNAs broadly expressed in cells of various species. However, the molecular mechanisms that link circRNAs with progression of papillary thyroid carcinoma (PTC) are not well understood. In the present study, we attempted to provide novel basis for targeted therapy for PTC from the aspect of circRNA-miRNA-mRNA interaction. We investigated the expression of circRNAs in five paired PTC tissues and normal tissues by microarray analysis. The circRNA microarray assay followed by qRT-PCR was used to verify the differential expression of hsa_circ_0059354, which is located on chromosome 20 and derived from RASSF2, and thus we named it circRASSF2. The qRT-PCR analysis was to investigate the expression pattern of circRASSF2 in PTC tissues and cell lines. Then the effects of circRASSF2 on cell proliferation and apoptosis were assessed in PTC in vitro. Furthermore, bioinformatics online programs predicted and luciferase reporter assays were used to validate the association of circRASSF2 and miR-1178 in PTC cells. In this study, circRASSF2 was observed to be upregulated in PTC tissues and cell lines. Knockdown of circRASSF2 inhibited cell proliferation and promoted cell apoptosis in PTC cells. Bioinformatics analysis predicted that there is a circRASSF2/miR-1178/TLR4 axis in PTC. A dual-luciferase reporter system validated the direct interaction of circRASSF2, miR-1178, and TLR4. Furthermore, circRASSF2 facilitates PTC progression in vivo. Importantly, we demonstrated that circRASSF2 was upregulated in serum exosomes from PTC patients. In summary, our study demonstrates that circRASSF2 modulates PTC progression through the miR-1178/TLR4 pathway. Our findings indicate that circRASSF2 may serve as a promising therapeutic target for the treatment of PTC patients.

Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm cooperation. This paper adopts a new data-driven approach to study how reinforcement learning can be utilized to jointly learn the cooperative communication and action policies for UAV swarms. Firstly, the communication policy of a UAV is defined, so that the UAV can autonomously decide the content of the message sent out according to its real-time status. Secondly, neural networks are designed to approximate the communication and action policies of the UAV, and their policy gradient optimization procedures are deduced, respectively. Then, a reinforcement learning algorithm is proposed to jointly learn the communication and action policies of UAV swarms. Numerical simulation results verify that the policies learned by the proposed algorithm are superior to the existing benchmark algorithms in terms of multi-target tracking performance, scalability in different scenarios, and robustness under communication failures.

Despite the proliferation of multi-agent deep reinforcement learning (MADRL), most existing typical methods do not scale well to the dynamics of agent populations. And as the population increases, the dimensional explosion of joint state-action and the complex interaction between agents make learning extremely cumbersome, which poses the scalability challenge for MADRL. This paper focuses on the scalability issue of MADRL with homogeneous agents. In a natural population, local interaction is a more feasible mode of interplay rather than global interaction. And inspired by the strategic interaction model in economics, we decompose the value function of each agent into the sum of the expected cumulative rewards of the interaction between the agent and each neighbor. This novel value function is decentralized and decomposable, which enables it to scale well to the dynamic changes in the number of large-scale agents. Hereby, the corresponding strategic interaction reinforcement learning algorithm (SIQ), is proposed to learn the optimal policy of each agent, wherein a neural network is employed to estimate the expected cumulative reward for the interaction between the agent and one of its neighbors. We test the validity of the proposed method in a mixed cooperative-competitive confrontation game through numerical experiments. Furthermore, the scalability comparison experiments illustrate that the scalability of the SIQ algorithm outperforms the independent learning and mean field reinforcement learning algorithms in multiple scenarios with different and dynamically changing numbers.

This paper proposes a novel unmanned aerial vehicle (UAV) cooperative decision-making method based on deep reinforcement learning for striking targets protected by enemy air defense systems (ADSs), wherein an improved exploration technique is proposed to address the challenge of sparse extrinsic rewards and enhance policy optimization. Firstly, we construct a multi-UAV decision-making environment that incorporates the dynamic and fine-grained ADS model. Then, to encourage the coordinated exploration of UAVs, we measure the novelty of global observation based on the modified random network distillation (RND) method and employ it as shared intrinsic rewards for all UAVs. The modification of novelty measurement is introducing an image-like representation that tightly integrates spatial and temporal information of the situation as the input of RND. Finally, the experimental results demonstrate that the proposed method develops effective UAV cooperative striking policies and excels baseline methods.