scholarly journals Pseudoxanthomonas kalamensis sp. nov., a novel gammaproteobacterium isolated from Johnston Atoll, North Pacific Ocean

2006 ◽  
Vol 56 (5) ◽  
pp. 1103-1107 ◽  
Author(s):  
Renee M. Harada ◽  
Sonia Campbell ◽  
Qing X. Li

An aerobic, mesophilic bacterium, strain JA40T, was isolated from soil contaminated with polycyclic aromatic hydrocarbons and polychlorinated biphenyls collected from Johnston Atoll in the North Pacific Ocean. The strain formed yellow-pigmented colonies on heterotrophic media. The cells were Gram-negative, non-motile, non-sporulating rods. The strain reduced nitrite to nitrous oxide, the DNA G+C content was 64 mol% and the dominant fatty acids were 15 : 0 iso, 17 : 1 iso cis7 and 11 : 0 iso 3-OH. DNA sequencing of 1457 nt of the 16S rRNA gene established that JA40T belongs in the genus Pseudoxanthomonas within the Xanthomonadaceae branch of the Gammaproteobacteria. Strain JA40T can be differentiated from other mesophilic species in the genus on the basis of its physiological and biochemical characteristics and distinctive fatty acid profile. Thus strain JA40T (=ATCC BAA-1031T=CIP 108476T) is the type strain of a novel species of the genus Pseudoxanthomonas, for which the name Pseudoxanthomonas kalamensis sp. nov. is proposed.

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.


2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  

2010 ◽  
Vol 37 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Robert H. Byrne ◽  
Sabine Mecking ◽  
Richard A. Feely ◽  
Xuewu Liu

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