Two step PCR method to exchange the resistance cassette of a vector

For co-transformation of two plasmids, both have to possess different antibiotic selection markers. If that is not the case, normally the gene of interest (GOI) is subcloned into another vector. Here we introduce a fast and easy method to exchange the antibiotic resistance cassette (ARC) in only two PCR steps.Method SummaryTo shuttle the antibiotic resistance cassette (ARC) from one vector to another, one can amplify the ARC of interest and use the resulting PCR-product as a primer pair for the next amplification step. Simply remove parental DNA template by DpnI digestion, transform PCR product directly in E. coli cells, select transformants on an appropriate agar plate and isolate target vector by plasmid preparation.

Improvement in Classification Performance Based on Target Vector Modification for All-Transfer Deep Learning

This paper proposes a target vector modification method for the all-transfer deep learning (ATDL) method. Deep neural networks (DNNs) have been used widely in many applications; however, the DNN has been known to be problematic when large amounts of training data are not available. Transfer learning can provide a solution to this problem. Previous methods regularize all layers, including the output layer, by estimating the relation vectors, which are then used instead of one-hot target vectors of the target domain. These vectors are estimated by averaging the target domain data of each target domain label in the output space. This method improves the classification performance, but it does not consider the relation between the relation vectors. From this point of view, we propose a relation vector modification based on constrained pairwise repulsive forces. High pairwise repulsive forces provide large distances between the relation vectors. In addition, the risk of divergence is mitigated by the constraint based on distributions of the output vectors of the target domain data. We apply our method to two simulation experiments and a disease classification using two-dimensional electrophoresis images. The experimental results show that reusing all layers through our estimation method is effective, especially for a significantly small number of the target domain data.

The Approximate k-List Problem

We study a generalization of the k-list problem, also known as the Generalized Birthday problem. In the k-list problem, one starts with k lists of binary vectors and has to find a set of vectors – one from each list – that sum to the all-zero target vector. In our generalized Approximate k-list problem, one has to find a set of vectors that sum to a vector of small Hamming weight ω. Thus, we relax the condition on the target vector and allow for some error positions. This in turn helps us to significantly reduce the size of the starting lists, which determines the memory consumption, and the running time as a function of ω. For ω = 0, our algorithm achieves the original k-list run-time/memory consumption, whereas for ω = n/2 it has polynomial complexity. As in the k-list case, our Approximate k-list algorithm is defined for all k = 2m,m > 1. Surprisingly, we also find an Approximate 3-list algorithm that improves in the runtime exponent compared to its 2-list counterpart for all 0 < ω < n/2. To the best of our knowledge this is the first such improvement of some variant of the notoriously hard 3-list problem. As an application of our algorithm we compute small weight multiples of a given polynomial with more flexible degree than with Wagner’s algorithm from Crypto 2002 and with smaller time/memory consumption than with Minder and Sinclair’s algorithm from SODA 2009.

Synthesis of Novel Peptide Dendrimers PDL-GB2 and PDL-G2

Peptide dendrimers are a novel type of macromolecules with precise structure, which can be used as drug target vector and controlled-release carrier. So it is valuable to study. In this paper, novel peptide dendrimers PDL-GB2 and PDL-G2 were prepared according to divergent procedure with four-orientation molecule as the core and L-lysine as the branch unit. And the structures were identified by1HNMR,13CNMR, MS, and elemental analysis.