Site-directed gene integration in transgenic zebrafish mediated by cre recombinase using a combination of mutant Lox sites

With current gene-transfer techniques in fish, insertion of DNA into the genome occurs randomly and in many instances at multiple sites. Associated position effects, copy number differences, and multiple gene interactions make gene expression experiments difficult to interpret and fish phenotype less predictable. To meet different fish engineering needs, we describe here a gene targeting model in zebrafish. At first, four target zebrafish lines, each harboring a single genomic lox71 target site, were generated by zebrafish transgenesis. The zygotes of transgenic zebrafish lines were coinjected with capped Cre mRNA and a knockin vector pZklox66RFP. Site-specific integration event happened from one target zebrafish line. In this line two integrant zebrafish were obtained from more than 80,000 targeted embryos (integrating efficiency about 10(-4) to 10(-5)) and confirmed to have a sole copy of the integrating DNA at the target genome site. Genomic polymerase chain reaction analysis and DNA sequencing verified the correct gene target events where lox71 and lox66 have accurately recombined into double mutant lox72 and wild-type loxP. Each integrant zebrafish chosen for analysis harbored the transgene rfp at the designated egfp concatenates. Although the Cre-mediated recombination is site specific, it is dependent on a randomly placed target site. That is, a genomic target cannot be preselected for integration based solely on its sequence. Conclusively, an rfp reporter gene was successfully inserted into the egfp target locus of zebrafish genome by Cre-lox-mediated recombination. This site-directed knockin system using the lox71/lox66 combination should be a promising gene-targeting platform serving various purposes in fish genetic engineering.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Direct observation of coherent spin transfer processes in an InGaAs/GaAs quantum well via two-color time-resolved Kerr rotation measurements

A two-color time-resolved Kerr rotation spectroscopy system was built, with a femtosecond Ti:sapphire laser and a photonic crystal fiber, to study coherent spin transfer processes in an InGaAs/GaAs quantum well sample. The femtosecond Ti:sapphire laser plays two roles: besides providing a pump beam with a tunable wavelength, it also excites the photonic crystal fiber to generate supercontinuum light ranging from 500 nm to 1600 nm, from which a probe beam with a desirable wavelength is selected with a suitable interference filter. With such a system, we studied spin transfer processes between two semiconductors of different gaps in an InGaAs/GaAs quantum well sample. We found that electron spins generated in the GaAs barrier were transferred coherently into the InGaAs quantum well. A model based on rate equations and Bloch-Torrey equations is used to describe the coherent spin transfer processes quantitatively. With this model, we obtain an effective electron spin accumulation time of 21 ps in the InGaAs quantum well.