Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Fashion, growth and welfare: An evolutionary approach

The task of this paper is to explore the interplay between fashion, consumer lifestyles and economic growth in the context of a world of technological change in which the menu of possibilities that consumers face is constantly changing and tending to increase in length. Our working definition of ‘fashion’ is simple, namely the tendency or behavioural norm of actors to adopt certain types or styles of customs or commodities nearly simultaneously, only to adopt a different type or style of custom or commodity in future periods. The demand spikes associated with fashion may pertain to newly introduced products or to products that have been around for some time; they may also occur in hybrid cases where a seemingly defunct product or genre is given a brief rebirth by being reincarnated in terms of a new technology

Growing Perfect Decagonal Quasicrystals by Local Rules

A local growth algorithm for a decagonal quasicrystal is presented. We show that a perfect Penrose tiling (PPT) layer can be grown on a decapod tiling layer by a three dimensional (3D) local rule growth. Once a PPT layer begins to form on the upper layer, successive 2D PPT layers can be added on top resulting in a perfect decagonal quasicrystalline structure in bulk with a point defect only on the bottom surface layer. Our growth rule shows that an ideal quasicrystal structure can be constructed by a local growth algorithm in 3D, contrary to the necessity of non-local information for a 2D PPT growth.Comment: 4pages, 2figure

Synthetic Antimicrobial Peptides Exhibit Two Different Binding Mechanisms to the Lipopolysaccharides Isolated from and

Circular dichroism and 1H NMR were used to investigate the interactions of a series of synthetic antimicrobial peptides (AMPs) with lipopolysaccharides (LPS) isolated from Pseudomonas aeruginosa and Klebsiella pneumoniae. Previous CD studies with AMPs containing only three Tic-Oic dipeptide units do not exhibit helical characteristics upon interacting with small unilamellar vesicles (SUVs) consisting of LPS. Increasing the number of Tic-Oic dipeptide units to six resulted in five analogues with CD spectra that exhibited helical characteristics on binding to LPS SUVs. Spectroscopic and in vitro inhibitory data suggest that there are two possible helical conformations resulting from two different AMP-LPS binding mechanisms. Mechanism one involves a helical binding conformation where the AMP binds LPS very strongly and is not efficiently transported across the LPS bilayer resulting in the loss of inhibitory activity. Mechanism two involves a helical binding conformation where the AMP binds LPS very loosely and is efficiently transported across the LPS bilayer resulting in an increase in inhibitory activity. Mechanism three involves a nonhelical binding conformation where the AMP binds LPS very loosely and is efficiently transported across the LPS bilayer resulting in an increase in inhibitory activity

Mice lacking C1q or C3 show accelerated rejection of minor H disparate skin grafts and resistance to induction of tolerance

Complement activation is known to have deleterious effects on organ transplantation. On the other hand, the complement system is also known to have an important role in regulating immune responses. The balance between these two opposing effects is critical in the context of transplantation. Here, we report that female mice deficient in C1q (C1qa(−/−)) or C3 (C3(−/−)) reject male syngeneic grafts (HY incompatible) at an accelerated rate compared with WT mice. Intranasal HY peptide administration, which induces tolerance to syngeneic male grafts in WT mice, fails to induce tolerance in C1qa(−/−) or C3(−/−) mice. The rejection of the male grafts correlated with the presence of HY D(b)Uty-specific CD8(+) T cells. Consistent with this, peptide-treated C1qa(−/−) and C3(−/−) female mice rejecting male grafts exhibited more antigen-specific CD8(+)IFN-γ(+) and CD8(+)IL-10(+) cells compared with WT females. This suggests that accumulation of IFN-γ- and IL-10-producing T cells may play a key role in mediating the ongoing inflammatory process and graft rejection. Interestingly, within the tolerized male skin grafts of peptide-treated WT mice, IFN-γ, C1q and C3 mRNA levels were higher compared to control female grafts. These results suggest that C1q and C3 facilitate the induction of intranasal tolerance