Transposition of ISY100

The insertion sequence ISY100 is a member of the IS630/Tc1/mariner superfamily of transposable elements. An in vivo transposition assay was set up in this study, confirming the TA target preference of ISY100 and showing that 30 bp from each end of ISY100 is sufficient for efficient transposition. Purified His-tagged transposase bound the transposon ends, protecting approximately 26 bp from cleavage by DNase I at each end. Two helix-turn-helix DNA binding motifs linked by an ‘AT-hook’-like sequence were predicted in the N-terminal domain of ISY100 transposase. Supercoiled plasmid containing ISY100 ends, and synthetic linear transposon ends were tested for cleavage by transposase in vitro. Cleavage products were observed and the cleavage sites were mapped. Linear DNA fragments containing single ISY100 ends were cleaved mainly one nucleotide inside the transposon end to produce a 3’ OH and one nucleotide outside the transposon end to produce a 5’ phosphate. Changes in the flanking TA dinucleotides at either one end or both ends of ISY100, reducing the efficiency of transposition in vivo. These changes also reduced the efficiency of cleavage in vitro. Changes at only one end inhibited cleavage at both ends implying communication between the two transposon ends. Synthetic pre-cut transposon ends were tested in an in vitro integration assay, and transposase catalysed the insertion of transposon 3’-OH ends into a target plasmid. Transposase mediated efficient integration of a mini-ISY100, pre-excised by transposase or restriction enzyme, into TA targets in vitro, confirming that excised transposon fragments are intermediates in the reaction. Target sequences of ISY100 from published data and this study were analyzed, yielding the consensus target sequence ADWTAWHT, in which the central TA is the duplicated target dinucleotide. When the Zif268 DNA-binding domain of Tn3 resolvase, transposition occurred into TA dinucleotides to one side of a Zif268 binding site with elevated frequency. This could be developed into a genetic tool for target-specific integration

Frosting Weights for Better Continual Training

Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one appealing property is the additive nature of ensemble models. In this paper, we propose two generic ensemble approaches, gradient boosting and meta-learning, to solve the catastrophic forgetting problem in tuning pre-trained neural network models