Huguangyan Maar Lake is a typical maar lake in the southeast of China. It is well preserved and not disturbed by anthropogenic activities. In this study, microbial community structures in sediment and water samples fr...Huguangyan Maar Lake is a typical maar lake in the southeast of China. It is well preserved and not disturbed by anthropogenic activities. In this study, microbial community structures in sediment and water samples from Huguangyan Maar Lake were investigated using a high-throughput sequencing method. We found significant differences between the microbial community compositions of the water and the sediment. The sediment samples contained more diverse Bacteria and Archaea than did the water samples. Actinobacteria, Betaproteobacteria, Cyanobacteria, and Deltaproteobacteria predominated in the water samples while Deltaproteobacteria, Anaerolineae, Nitrospira, and Dehalococcoidia were the major bacterial groups in the sediment. As for Archaea, Woesearchaeota (DHVEG-6), unclassified Archaea, and Deep Sea Euryarchaeotic Group were detected at higher abundances in the water, whereas the Miscellaneous Crenarchaeotic Group, Thermoplasmata, and Methanomicrobia were significantly more abundant in the sediment. Interactions between Bacteria and Archaea were common in both the water column and the sediment. The concentrations of major nutrients (NO^3-, PO4^3-, SiO3^2- and NH4^+) shaped the microbial population structures in the water. At the higher phylogenetic levels including phylum and class, many of the dominant groups were those that were also abundant in other lakes;however, novel microbial populations (unclassified) were often seen at the lower phylogenetic levels. Our study lays a foundation for examining microbial biogeochemical cycling in sequestered lakes or reservoirs.展开更多
Sediment samples obtained from the South Mid-Atlantic Ridge were analyzed for the major and trace elements by inductively coupled plasma atomic emission spectroscopy and inductively coupled plasma mass spectrometry. R...Sediment samples obtained from the South Mid-Atlantic Ridge were analyzed for the major and trace elements by inductively coupled plasma atomic emission spectroscopy and inductively coupled plasma mass spectrometry. Results revealed that the contents of elements(e.g., Fe, Mn, Cu, Zn, V, Co) were high in samples 22V-TVG10 and 26V-TVG05 from the sites near the hydrothermal areas, and low in sample 22V-TVG14, which was collected far from the hydrothermal areas. The contents of Ca, Sr and Ba in the samples showed opposite trends. A positive correlation between the concentrations of metallic elements(Cu, Zn, Co, Ni, Pb, V) and Fe in the samples were observed. These results are consistent with chemical evolution of the dispersing hydrothermal plume.展开更多
Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep...Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep learning to recognize modulation types of LPI radar signals efficiently.A novel algorithm combining dual efficient network(DEN)and non-local means(NLM)denoising was proposed for the identification and selection of LPI radar signals.Time-domain signals for 12 radar modulation types were simulated,adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios.On this basis,the noisy radar signals undergo Choi-Williams distribution(CWD)time-frequency transformation,converting the signals into two-dimensional(2D)time-frequency images(TFIs).The TFIs are then denoised using the NLM algorithm.Finally,the denoised data is fed into the designed DEN for training and testing,with the selection results output through a softmax classifier.Simulation results demonstrate that at an SNR of-8 dB,the algorithm can achieve a recognition accuracy of 97.22%for LPI radar signals,exhibiting excellent performance under low SNR conditions.Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes.This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.41576123,41706129)the Guangdong Natural Science Foundation(Nos.2015A030313326,2016A030312004)+2 种基金the International Science and Technology Cooperation Project(No.GASI-IPOVI-04)the Project of Enhancing School with Innovation of Guangdong Ocean University(No.GDOU2016050243)the Program for Scientific Research Start-Up Funds of Guangdong Ocean University(No.E15030)
文摘Huguangyan Maar Lake is a typical maar lake in the southeast of China. It is well preserved and not disturbed by anthropogenic activities. In this study, microbial community structures in sediment and water samples from Huguangyan Maar Lake were investigated using a high-throughput sequencing method. We found significant differences between the microbial community compositions of the water and the sediment. The sediment samples contained more diverse Bacteria and Archaea than did the water samples. Actinobacteria, Betaproteobacteria, Cyanobacteria, and Deltaproteobacteria predominated in the water samples while Deltaproteobacteria, Anaerolineae, Nitrospira, and Dehalococcoidia were the major bacterial groups in the sediment. As for Archaea, Woesearchaeota (DHVEG-6), unclassified Archaea, and Deep Sea Euryarchaeotic Group were detected at higher abundances in the water, whereas the Miscellaneous Crenarchaeotic Group, Thermoplasmata, and Methanomicrobia were significantly more abundant in the sediment. Interactions between Bacteria and Archaea were common in both the water column and the sediment. The concentrations of major nutrients (NO^3-, PO4^3-, SiO3^2- and NH4^+) shaped the microbial population structures in the water. At the higher phylogenetic levels including phylum and class, many of the dominant groups were those that were also abundant in other lakes;however, novel microbial populations (unclassified) were often seen at the lower phylogenetic levels. Our study lays a foundation for examining microbial biogeochemical cycling in sequestered lakes or reservoirs.
基金the National Natural Science Foundation of China (No. 41306053)the Open Fund of the Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences (Nos. MGE 2015KG04 and MGE2015KG01)+2 种基金the Open Fund of the Key Laboratory of Submarine Geosciences, State Oceanic Administration, People’s Republic of China (No. KSLG 1503)the Special Fund for the Taishan Scholar Program of Shandong Province (No. ts201511061)The authors would like to thank the crews of the COMRA cruises (DY115-22 and DY115-26)
文摘Sediment samples obtained from the South Mid-Atlantic Ridge were analyzed for the major and trace elements by inductively coupled plasma atomic emission spectroscopy and inductively coupled plasma mass spectrometry. Results revealed that the contents of elements(e.g., Fe, Mn, Cu, Zn, V, Co) were high in samples 22V-TVG10 and 26V-TVG05 from the sites near the hydrothermal areas, and low in sample 22V-TVG14, which was collected far from the hydrothermal areas. The contents of Ca, Sr and Ba in the samples showed opposite trends. A positive correlation between the concentrations of metallic elements(Cu, Zn, Co, Ni, Pb, V) and Fe in the samples were observed. These results are consistent with chemical evolution of the dispersing hydrothermal plume.
文摘Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep learning to recognize modulation types of LPI radar signals efficiently.A novel algorithm combining dual efficient network(DEN)and non-local means(NLM)denoising was proposed for the identification and selection of LPI radar signals.Time-domain signals for 12 radar modulation types were simulated,adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios.On this basis,the noisy radar signals undergo Choi-Williams distribution(CWD)time-frequency transformation,converting the signals into two-dimensional(2D)time-frequency images(TFIs).The TFIs are then denoised using the NLM algorithm.Finally,the denoised data is fed into the designed DEN for training and testing,with the selection results output through a softmax classifier.Simulation results demonstrate that at an SNR of-8 dB,the algorithm can achieve a recognition accuracy of 97.22%for LPI radar signals,exhibiting excellent performance under low SNR conditions.Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes.This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.
