No abstract

Anaerobic oxidation of ammonium (anammox) is recognized as an important process for nitrogen (N) cycling, yet its role in agricultural ecosystems, which are intensively fertilized, remains unclear. In this study, we investigated the presence, activity, functional gene abundance and role of anammox bacteria in rhizosphere and non-rhizosphere paddy soils using catalyzed reporter deposition–fluorescence in situ hybridization, isotope-tracing technique, quantitative PCR assay and 16S rRNA gene clone libraries. Results showed that rhizosphere anammox contributed to 31–41% N2 production with activities of 0.33–0.64 nmol N2 g−1 soil h−1, whereas the non-rhizosphere anammox bacteria contributed to only 2–3% N2 production with lower activities of 0.08–0.26 nmol N2 g−1 soil h−1. Higher anammox bacterial cells were observed (0.75–1.4 × 107 copies g−1 soil) in the rhizosphere, which were twofold higher compared with the non-rhizosphere soil (3.7–5.9 × 106 copies g−1 soil). Phylogenetic analysis of the anammox bacterial 16S rRNA genes indicated that two genera of ‘Candidatus Kuenenia' and ‘Candidatus Brocadia' and the family of Planctomycetaceae were identified. We suggest the rhizosphere provides a favorable niche for anammox bacteria, which are important to N cycling, but were previously largely overlooked.

In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requirements on the delay expectation and resource allocation efficiency of the Random Access (RA) mechanisms of the IoT communication stack. A grant-free non-orthogonal random access (NORA) system is considered in this paper, which could simultaneously reduce the access delay and support more Machine Type Communication (MTC) devices with limited resources. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In the DNN-MP-BSBL algorithm, the iterative message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the trained weights could alleviate the convergence problem of the MP-BSBL algorithm, especially on crowded RA scenarios. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations, indicating its advantages for low-latency grant-free NORA systems.

No abstract