Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust min...Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.展开更多
ObjectiveTo investigate the anticancer property of marine sediment actinomycetes against two different breast cancer cell lines.MethodsIn vitro anticancer activity was carried out against breast (MCF-7 and MDA-MB-231)...ObjectiveTo investigate the anticancer property of marine sediment actinomycetes against two different breast cancer cell lines.MethodsIn vitro anticancer activity was carried out against breast (MCF-7 and MDA-MB-231) cancer cell lines. Partial sequences of the 16s rRNA gene, phylogenetic tree construction, multiple sequence analysis and secondary structure analysis were also carried out with the actinomycetes isolates.ResultsOf the selected five actinomycete isolates, ACT01 and ACT02 showed the IC50 value with (10.13±0.92) and (22.34±5.82) μg/mL concentrations, respectively for MCF-7 cell line at 48 h, but ACT01 showed the minimum (18.54±2.49 μg/mL) level of IC50 value with MDA-MB-231 cell line. Further, the 16s rRNA partial sequences of ACT01, ACT02, ACT03, ACT04 and ACT05 isolates were also deposited in NCBI data bank with the accession numbers of GQ478246, GQ478247, GQ478248, GQ478249 and GQ478250, respectively. The phylogenetic tree analysis showed that, the isolates of ACT02 and ACT03 were represented in group I and III, respectively, but ACT01 and ACT02 were represented in group II. The multiple sequence alignment of the actinomycete isolates showed that, the maximum identical conserved regions were identified with the nucleotide regions of 125 to 221st base pairs, 65 to 119th base pairs and 55, 48 and 31st base pairs. Secondary structure prediction of the 16s rRNA showed that, the maximum free energy was consumed with ACT03 isolate (-45.4 kkal/mol) and the minimum free energy was consumed with ACT04 isolate (?7.6 kkal/mol).ConclusionsThe actinomycete isolates of ACT01 and ACT02 (GQ478246 and GQ478247) which are isolated from sediment sample can be further used as anticancer agents against breast cancer cell lines.展开更多
基金Project(51874353)supported by the National Natural Science Foundation of ChinaProject(GCX20190898Y)supported by Mittal Student Innovation Project,China。
文摘Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.
基金supported by Indian Council of Medical Research,New Delhi(grant No.59/6/200/BMS/TRM)
文摘ObjectiveTo investigate the anticancer property of marine sediment actinomycetes against two different breast cancer cell lines.MethodsIn vitro anticancer activity was carried out against breast (MCF-7 and MDA-MB-231) cancer cell lines. Partial sequences of the 16s rRNA gene, phylogenetic tree construction, multiple sequence analysis and secondary structure analysis were also carried out with the actinomycetes isolates.ResultsOf the selected five actinomycete isolates, ACT01 and ACT02 showed the IC50 value with (10.13±0.92) and (22.34±5.82) μg/mL concentrations, respectively for MCF-7 cell line at 48 h, but ACT01 showed the minimum (18.54±2.49 μg/mL) level of IC50 value with MDA-MB-231 cell line. Further, the 16s rRNA partial sequences of ACT01, ACT02, ACT03, ACT04 and ACT05 isolates were also deposited in NCBI data bank with the accession numbers of GQ478246, GQ478247, GQ478248, GQ478249 and GQ478250, respectively. The phylogenetic tree analysis showed that, the isolates of ACT02 and ACT03 were represented in group I and III, respectively, but ACT01 and ACT02 were represented in group II. The multiple sequence alignment of the actinomycete isolates showed that, the maximum identical conserved regions were identified with the nucleotide regions of 125 to 221st base pairs, 65 to 119th base pairs and 55, 48 and 31st base pairs. Secondary structure prediction of the 16s rRNA showed that, the maximum free energy was consumed with ACT03 isolate (-45.4 kkal/mol) and the minimum free energy was consumed with ACT04 isolate (?7.6 kkal/mol).ConclusionsThe actinomycete isolates of ACT01 and ACT02 (GQ478246 and GQ478247) which are isolated from sediment sample can be further used as anticancer agents against breast cancer cell lines.
