Application Of Secretion Research Articles

Binary Linear Codes With Two Weights

Linear codes have wide applications in secret sharing schemes and authentication codes. In this letter, a class of binary linear codes with two weights is presented. The weight distribution of this class of binary linear codes is settled. The dual codes are also studied and proved to be optimal or almost optimal.

Linear Codes From Some 2-Designs

A classical method of constructing a linear code over $\gf(q)$ with a $t$-design is to use the incidence matrix of the $t$-design as a generator matrix over $\gf(q)$ of the code. This approach has been extensively investigated in the literature. In this paper, a different method of constructing linear codes using specific classes of $2$-designs is studied, and linear codes with a few weights are obtained from almost difference sets, difference sets, and a type of $2$-designs associated to semibent functions. Two families of the codes obtained in this paper are optimal. The linear codes presented in this paper have applications in secret sharing and authentication schemes, in addition to their applications in consumer electronics, communication and data storage systems. A coding-theory approach to the characterisation of highly nonlinear Boolean functions is presented.

Application of secret sharing and network coding to wiretap networks

Inspired by the cryptography method of confidential communication and the idea of secret shcaring, the authors proposed a secure communication scheme suitable to the wiretap network that employed network coding for data transmission.The proposed scheme used the original random bits to generate a new random bit string for enrypting the source messages, and then mixed the original random bit string with a secret string which was generated by the obtianed ciphertexts. Lastly, efficient use of network capacity with randong linear network coding was achieved. What's more, our scheme neither resorted to a secret channel which transmited the secret key to sinks, nor constucted linear network coding to meet special conditions which depended on network topology.

Insulin and memory inLymnaea

The pond snail, Lymnaea stagnalis, is capable of learning conditioned taste aversion (CTA) and consolidating this CTA into long-term memory (LTM). The DNA microarray experiments showed that some of molluscan insulin-related peptides (MIPs) were up-regulated in snails exhibiting CTA-LTM. On the other hand, the electrophysiological experiments showed that application of secretions from the MIPs-containing cells evoked long-term potentiation (LTP) at the synapses between the cerebral giant cell (a key interneuron for CTA) and the B1 motoneuron (a buccal motoneuron). We thus hypothesized that MIPs and MIP receptors play an important role at the synapses, probably underlying the CTA-LTM consolidation process. To examine this hypothesis, we applied the antibody, which recognizes the binding site of mammalian insulin receptors and is thought to cross-react MIP receptors, to the Lymnaea CNS. Our present data showed that an application of the antibody for insulin receptors to the isolated CNS blocked LTP, and that an injection of the antibody into the Lymnaea abdominal cavity inhibited LTM consolidation, but not CTA formation.