基于浮标数据的卫星雷达高度计海浪波高数据评价与校正

卫星雷达高度计是海浪有效波高（significant wave height,SWH）观测的重要手段之一,本文利用时空匹配方法对T/P、Jason-1、Envisat、Jason-2、Cryosat-2和HY-2A共6颗卫星雷达高度计SWH数据与NDBC（National Data Buoy Center,NDBC）浮标SWH数据进行对比验证,并对雷达高度计SWH数据进行校正。全部卫星雷达高度计SWH数据时间跨度为1992年9月25日到2015年9月1日,对比验证NDBC浮标共53个,包括7个大洋浮标。精度评价发现除T/P外,各卫星雷达高度计SWH的RMSE都在0.4~0.5 m之间,经过校正后,RMSE都有显著下降,下降程度最大为13.82%;对于大洋浮标,评价结果RMSE在0.20~0.28 m之间,结果明显优于全部NDBC浮标的精度评价结果;HY-2A卫星雷达高度计SWH在经过校正后数据质量与国外其他5颗卫星雷达高度计SWH数据质量差异较小。     

Biodegradation of <Emphasis Type="Italic">Enteromorpha</Emphasis> polysaccharides by intestinal micro-community from <Emphasis Type="Italic">Siganus oramin</Emphasis>

Micro-communities are supposed to have more potential functions of biodegradation of polysaccharides than single strain; however, the intestinal micro-communities involved in the biodegradation of Enteromorpha polysaccharides (EP) were seldom reported. In order to obtain the EP-degrading micro-community, the intestines of Siganus oramin was obtained to isolate the micro-communities, which were enriched by 0.3% of EP as the sole carbon source. A stable micro-community with EP degradative capability was achieved after seven generations of subculture, named H1. Results showed that H1 was able to degrade 75% of EP within 24 hours, and the activity of EP lyases reached 500 U mL^?1 in 32 hours. With denaturing gradient gel electrophoresis (DGGE) and 16S rRNA gene clone library analysis, ten bacteria closely related to Marinomonas pontica, Microbacterium sp., Leucobacter chironomi, Cyclobacterium sp., Algoriphagus winogradskyi, Pseudoalteromonas sp. and Vibrio sp. were determined. Furthermore, compared with the DGGE bands sequence and the clone library analysis, the dominant bacteria of the EP-biodegrading micro- community were Pseudoalteromonas sp. and Vibrio sp., with the respective proportion of 38% and 46%, and they should play an important role in EP degradation together with other degrading bacteria in the micro-community H1.     

Antialgal and antilarval activities of bioactive compounds extracted from the marine dinoflagellate <Emphasis Type="Italic">Amphidinium carterae</Emphasis>

With the global ban on the application of organotin-based marine coatings by the International Maritime Organization, the development of environmentally friendly, low-toxic and nontoxic antifouling compounds for marine industries has become an urgent need. Marine microorganisms have been considered as a potential source of natural antifoulants. In this study, the antifouling potential of marine dinoflagellate Amphidinium carterae, the toxic and red-tide microalgae, was investigated. We performed a series of operations to extract the bioactive substances from Amphidinium carterae and tested their antialgal and antilarval activities. The crude extract of Amphidinium carterae showed significant antialgal activity and the EC₅₀ value against Skeletonema costatum was 55.4 μg mL^?1. After purification, the isolated bioactive substances (the organic extract C) exhibited much higher antialgal and antilarval activities with EC₅₀ of 12.9 μg mL^?1 against Skeletonema costatum and LC₅₀ of 15.1 μg mL^?1 against Amphibalanus amphitrite larvae. Subsequently, IR, Q-TOFMS, and GC-MS were utilized for the structural elucidation of the bioactive compounds, and a series of unsaturated and saturated 16- to 22-carbon fatty acids were detected. The data suggested the bioactive compounds isolated from Amphidinium carterae exhibited a significant inhibiting effect against the diatom Skeletonema costatum and Amphibalanus amphitrite larvae, and could be substitutes for persistent, toxic antifouling compounds.     

New variogram modeling method using MGGP and SVR

A critical step for kriging in geostatistics is estimation of the variogram. Traditional variogram modeling comprise of the experimental variogram calculation, appropriate variogram model selection and model parameter determination. Selecting of the variogram model and fitting of model parameters is the most controversial aspect of geostatistics. Shapes of valid variogram models are finite, and sometimes, the optimal shape of the model can not be fitted, leading to reduced estimation accuracy. In this paper, a new method is presented to automatically construct a model shape and fit model parameters to experimental variograms using Support Vector Regression (SVR) and Multi-Gene Genetic Programming (MGGP). The proposed method does not require the selection of a variogram model and can directly provide the model shape and parameters of the optimal variogram. The validity of the proposed method is demonstrated in a number of cases.