本文首次报道苍山地区两种短翅蝗虫染色体,结果表明:突缘拟凹背蝗(Pseudoptygonotus prominemaginis Zheng et Mao)2n(♂)=22+XO,全部是端部着丝点染色体,染色体组式为2L+8M+S+X,红胫缺背蝗(Anaptygus rufitibialusZheng et Mao)染色体...本文首次报道苍山地区两种短翅蝗虫染色体,结果表明:突缘拟凹背蝗(Pseudoptygonotus prominemaginis Zheng et Mao)2n(♂)=22+XO,全部是端部着丝点染色体,染色体组式为2L+8M+S+X,红胫缺背蝗(Anaptygus rufitibialusZheng et Mao)染色体数目为2n(♂)=16+XO,常染色体类型为两类,中央着丝点染色体(m,6条)和端着丝点染色体(T,10条);性染色体类型为端着丝点染色体,染色体组式为3L+3M+2S+X;两种短翅蝗虫的减数分裂染色体交叉频率以1位点和2位点的交叉居多。展开更多
文中制备了一种用于粒子分离的介电电泳微流控芯片,利用粒子介电性质不同实现粒子批量、高效分离。采用MEMS工艺,由光刻有电极的ITO玻璃基底和PDMS微通道制备而成。在此基础上,测定了当缓冲溶液的电导率为1μS/cm、交流信号电压为10 V...文中制备了一种用于粒子分离的介电电泳微流控芯片,利用粒子介电性质不同实现粒子批量、高效分离。采用MEMS工艺,由光刻有电极的ITO玻璃基底和PDMS微通道制备而成。在此基础上,测定了当缓冲溶液的电导率为1μS/cm、交流信号电压为10 V时聚苯乙烯小球和酵母菌的正负介电泳响应,确定了两种微粒的分离条件:酵母菌细胞在1~50 k Hz时表现负介电泳响应,50 k Hz^5 MHz时表现正介电泳响应,50 k Hz为交叉频率;聚苯乙烯小球在1 k Hz^5 MHz始终表现负介电泳响应。选取10 V、5 MHz交流电压信号作为分离条件,对直径均为5μm的聚苯乙烯小球和酵母菌进行了分离,分离效率达到92.4%。展开更多
To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the freque...To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.展开更多
文摘本文首次报道苍山地区两种短翅蝗虫染色体,结果表明:突缘拟凹背蝗(Pseudoptygonotus prominemaginis Zheng et Mao)2n(♂)=22+XO,全部是端部着丝点染色体,染色体组式为2L+8M+S+X,红胫缺背蝗(Anaptygus rufitibialusZheng et Mao)染色体数目为2n(♂)=16+XO,常染色体类型为两类,中央着丝点染色体(m,6条)和端着丝点染色体(T,10条);性染色体类型为端着丝点染色体,染色体组式为3L+3M+2S+X;两种短翅蝗虫的减数分裂染色体交叉频率以1位点和2位点的交叉居多。
文摘文中制备了一种用于粒子分离的介电电泳微流控芯片,利用粒子介电性质不同实现粒子批量、高效分离。采用MEMS工艺,由光刻有电极的ITO玻璃基底和PDMS微通道制备而成。在此基础上,测定了当缓冲溶液的电导率为1μS/cm、交流信号电压为10 V时聚苯乙烯小球和酵母菌的正负介电泳响应,确定了两种微粒的分离条件:酵母菌细胞在1~50 k Hz时表现负介电泳响应,50 k Hz^5 MHz时表现正介电泳响应,50 k Hz为交叉频率;聚苯乙烯小球在1 k Hz^5 MHz始终表现负介电泳响应。选取10 V、5 MHz交流电压信号作为分离条件,对直径均为5μm的聚苯乙烯小球和酵母菌进行了分离,分离效率达到92.4%。
基金supported by the National Natural Science Foundation of China(No.NSFC 41204101)Open Projects Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(No.PLN201733)+1 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2015051)Open Projects Fund of the Natural Gas and Geology Key Laboratory of Sichuan Province(No.2015trqdz03)
文摘To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.
