Giant catfish Pangasianodon gigas growth hormone-encoding cDNA: cloning and sequencing by one-sided polymerase chain reaction

cDNA clones encoding giant catfish (Pangasianodon gigas) growth hormone (GH) have been isolated using a polymerase chain reaction (PCR) strategy. Pairwise combinations of degenerate and general primers allowed for the amplification of regions both 3' and 5' to the point of entry into the message. The amplified PCR products were cloned and sequenced. The cDNA sequence was found to encode a polypeptide of 200 amino acids (aa), including a putative signal peptide of 22 aa. The 5' and 3' untranslated regions of the message are 58 and 515 nucleotides long, respectively. The giant catfish GH displays the highest aa sequence homology with the carp GH, with 80% of sequence identity. Moreover, giant catfish GH has structural features in common with both mammalian and avian GH polypeptides, and also contains the domains of conserved sequence found in other GH.     

Computation of Peak Output for Inputs Restricted in L2 and L∞ Norms Using Finite Difference Schemes and Convex Optimization

Control systems designed by the principle of matching gives rise to problems of evaluating the peak output. This paper proposes a practical method for computing the peak output of linear time-invariant and non-anticipative systems for a class of possible sets that are characterized with many bounding conditions on the two- and/or the infinity-norms of the inputs and their derivatives. The original infinite-dimensional convex optimization problem is approximated as a large-scale convex programme defined in a Euclidean space, which are associated with sparse matrices and thus can be solved efficiently in practice. The numerical results show that the method performs satisfactorily, and that using a possible set with many bounding conditions can help to reduce the design conservatism and thereby yield a better match.     

Dynamic Probabilistic Caching Algorithm with Content Priorities for Content‐Centric Networks

This paper presents a caching algorithm that offers better reconstructed data quality to the requesters than a probabilistic caching scheme while maintaining comparable network performance. It decides whether an incoming data packet must be cached based on the dynamic caching probability, which is adjusted according to the priorities of content carried by the data packet, the uncertainty of content popularities, and the records of cache events in the router. The adaptation of caching probability depends on the priorities of content, the multiplication factor adaptation, and the addition factor adaptation. The multiplication factor adaptation is computed from an instantaneous cache‐hit ratio, whereas the addition factor adaptation relies on a multiplication factor, popularities of requested contents, a cache‐hit ratio, and a cache‐miss ratio. We evaluate the performance of the caching algorithm by comparing it with previous caching schemes in network simulation. The simulation results indicate that our proposed caching algorithm surpasses previous schemes in terms of data quality and is comparable in terms of network performance.     

Computation of Peak Output for Inputs Restricted in L2 and L∞ Norms Using Finite Difference Schemes and Convex Optimization

Control systems designed by the principle of matching gives rise to problems of evaluating the peak output.This paper proposes a practical method for computing the peak output of linear time-invariant and non-anticipative systems for a class of possible sets that are characterized with many bounding conditions on the two- and/or the infinity-norms of the inputs and their derivatives.The original infinite-dimensional convex optimization problem is approximated as a large-scale convex programme defined in a Euclidean space,which are associated with sparse matrices and thus can be solved efficiently in practice.The numerical results show that the method performs satisfactorily,and that using a possible set with many bounding conditions can help to reduce the design conservatism and thereby yield a better match.     

Computation of Peak Output for Inputs Restricted in L_2 and L_∞ Norms Using Finite Difference Schemes and Convex Optimization

Control systems designed by the principle of matching gives rise to problems of evaluating the peak output. This paper proposes a practical method for computing the peak output of linear time-invariant and non-anticipative systems for a class of possible sets that are characterized with many bounding conditions on the two-and/or the infinity-norms of the inputs and their derivatives. The original infinite-dimensional convex optimization problem is approximated as a large-scale convex programme deffned in a Euclidean space, which are associated with sparse matrices and thus can be solved effciently in practice. The numerical results show that the method performs satisfactorily, and that using a possible set with many bounding conditions can help to reduce the design conservatism and thereby yield a better match.