An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

3D Magneto-Hydrodynamic Simulations of Parker Instability with Cosmic Rays

This study investigates Parker instability in an interstellar medium (ISM) near the Galactic plane using three-dimensional magneto-hydrodynamic simulations. Parker instability arises from the presence of a magnetic field in a plasma, wherein the magnetic buoyant pressure expels the gas and cause the gas to move along the field lines. The process is thought to induce the formation of giant molecular clouds in the Galaxy. In this study, the effects of cosmic-ray (CR) diffusion are examined. The ISM at equilibrium is assumed to comprise a plasma fluid and a CR fluid at various temperatures, with a uniform magnetic field passing through it in the azimuthal direction of the Galactic disk. After a small perturbation, the unstable gas aggregates at the footpoint of the magnetic fields and forms dense blobs. The growth rate of the instability increases with the strength of the CR diffusion. The formation of dense clouds is enhanced by the effect of cosmic rays (CRs), whereas the shape of the clouds depends sensitively on the initial conditions of perturbation.Comment: 4 pages, Computer Physics Communications 2011, 182, p177-17

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Production of N-acetyl-D-neuraminic acid using two sequential enzymes overexpressed as double-tagged fusion proteins

<p>Abstract</p> <p>Background</p> <p>Two sequential enzymes in the production of sialic acids, N-acetyl-D-glucosamine 2-epimerase (GlcNAc 2-epimerase) and <it>N</it>-acetyl-D-neuraminic acid aldolase (Neu5Ac aldolase), were overexpressed as double-tagged gene fusions. Both were tagged with glutathione S-transferase (GST) at the N-terminus, but at the C-terminus, one was tagged with five contiguous aspartate residues (5D), and the other with five contiguous arginine residues (5R).</p> <p>Results</p> <p>Both fusion proteins were overexpressed in <it>Escherichia coli </it>and retained enzymatic activity. The fusions were designed so their surfaces were charged under enzyme reaction conditions, which allowed isolation and immobilization in a single step, through a simple capture with either an anionic or a cationic exchanger (Sepharose Q or Sepharose SP) that electrostatically bound the 5D or 5R tag. The introduction of double tags only marginally altered the affinity of the enzymes for their substrates, and the double-tagged proteins were enzymatically active in both soluble and immobilized forms. Combined use of the fusion proteins led to the production of <it>N</it>-acetyl-D-neuraminic acid (Neu5Ac) from <it>N</it>-acetyl-D-glucosamine (GlcNAc).</p> <p>Conclusion</p> <p>Double-tagged gene fusions were overexpressed to yield two enzymes that perform sequential steps in sialic acid synthesis. The proteins were easily immobilized via ionic tags onto ionic exchange resins and could thus be purified by direct capture from crude protein extracts. The immobilized, double-tagged proteins were effective for one-pot enzymatic production of sialic acid.</p

Co-Overexpression of Cyclooxygenase-2 and Microsomal Prostaglandin E Synthase-1 Adversely Affects the Postoperative Survival in Non-small Cell Lung Cancer

IntroductionCyclooxygenase (COX)-2 and microsomal prostaglandin E synthase (mPGES)-1 have been found to be overexpressed in non-small cell lung cancer (NSCLC). The aim of this study was to investigate the expression profiles of COX-2 and mPGES-1 and their correlation with the clinical characteristics and survival outcomes in patients with resected NSCLC.Methods/ResultsSeventy-nine paired adjacent normal-tumor matched samples were prospectively procured from patients undergoing surgery for NSCLC. The protein levels of COX-2 and mPGES-1 were assessed by Western blot analysis. Overexpression in the tumor sample was defined as more than twofold increase in protein expression compared with the corresponding adjacent normal tissue. Co-overexpression of COX-2 and mPGES-1 were further confirmed by immunohistochemistry. COX-2 was overexpressed in 58% and mPGES-1 in 70% of the tumor samples (p < 0.0001). Co-overexpression of mPGES-1 and COX-2 was noted in 43%, and they were unrelated to each other (p = 0.232). Co-overexpression of both proteins was significantly associated with less tumor differentiation (p = 0.046), tumor size larger than 5 cm (p = 0.038), and worse survival status during the follow-up (p = 0.036). Multivariate analysis showed that in addition to overall stage, co-overexpression of both proteins adversely affected the overall (hazard ratio, 2.40; p = 0.045) and disease-free survivals (hazard ratio, 2.27; p = 0.029).ConclusionsOverexpression of either COX-2 or mPGES-1 is common but unrelated in NSCLC. Co-overexpression of both COX-2 and mPGES-1 adversely affects postoperative overall and disease-free survivals

Validation of the Action Research Arm Test using item response theory in patients after stroke

Objective: To validate the unidimensionality of the Action Research Arm Test (ARAT) using Mokken analysis and to examine whether scores of the ARAT can be transformed into interval scores using Rasch analysis. Subjects and methods: A total of 351 patients with stroke were recruited from 5 rehabilitation departments located in 4 regions of Taiwan. The 19-item ARAT was administered to all the subjects by a physical therapist. The data were analysed using item response theory by non-parametric Mokken analysis followed by Rasch analysis. Results: The results supported a unidimensional scale of the 19-item ARAT by Mokken analysis, with the scalability coefficient H = 0.95. Except for the item pinch ball bearing 3rd finger and thumb'', the remaining 18 items have a consistently hierarchical order along the upper extremity function's continuum. In contrast, the Rasch analysis, with a stepwise deletion of misfit items, showed that only 4 items (grasp ball'', grasp block 5 cm(3)'', grasp block 2.5 cm(3)'', and grip tube 1 cm(3)'') fit the Rasch rating scale model's expectations. Conclusion: Our findings indicated that the 19-item ARAT constituted a unidimensional construct measuring upper extremity function in stroke patients. However, the results did not support the premise that the raw sum scores of the ARAT can be transformed into interval Rasch scores. Thus, the raw sum scores of the ARAT can provide information only about order of patients on their upper extremity functional abilities, but not represent each patient's exact functioning