[Expression of long non-coding RNA LINC00460 in laryngeal squamous cell carcinoma tissue and its clinical significance].

Objective:To understand the expression of LINC00460 in laryngeal squamous cell carcinoma (LSCC), and to discuss the role of LINC00460 in carcinogenesis and development of laryngeal squamous cell carcinoma. Method:The expression of LINC00460 in 68 laryngeal squamous cell carcinoma tissues and paired adjacent normal tissues were examined by real-time PCR. The relationship between the expression levels of LINC00460 and the clinicopathological features including clinical stage, histological grade, pathological type, and lymph node metastasis in LSCC was analyzed according to the clinical data. Result:The expression of LINC00460 was significantly upregulated in the LSCC tissue compared with that of adjacent normal mucosal tissue (P=0.006). There were no statistical differences of the quantity of LINC00460 expression among supraglottic, glottic and subglottic LSCC (P>0.05). Moreover, LINC00460 had no significant changes in poorly differentiated LSCC when compared with that of well and moderately differentiated LSCC (P>0.05). Furthermore, the expression of LINC00460 in LSCC with lymph node metastasis had no significant changes when compared with that without of lymph node metastasis (P>0.05). Notably, LINC00460 expression in T1+T2 stages patients were significantly lower than T3+T4 stages (P<0.05). Conclusiont:Upregulation of long non-coding RNA LINC00460 might contribute to the carcinogenesis and development of LSCC, furthermore play important biological function.

[Comparison of gender specific structure profiles of immunoglobulin G N-glycans].

Objective: To analyze and compare the gender specific structure profiles of immunoglobulin G(IgG) N-glycans and provide evidence to understand the distribution of the structure of IgG N-glycans in general population. Methods: A cluster sampling was conducted to randomly select 669 participants from Beijing Xuanwu Hospital during January-June 2012. Venous blood sample (5 ml) was collected from each participant with vacuum negative pressure tube containing EDTA. Plasma IgG N-glycome was determined using ultra-performance liquid chromatography (UPLC) on Waters BEH Glycan chromatography column and mass spectrometry after the separation, purification and labeling. Finally, we got 24 chromatographic peaks. Each peak represented a different carbohydrate structure. Independent sample t-test or nonparametric test were used to compare the gender specific difference, P<0.05 was regarded as significant. Results: A total of 669 healthy participants were included, including 235 men (35.13%) and 434 women (64.87%). The average age of the participants was (44.23±9.82) years. The core fucosylation level and the galactosylation level of females were higher than those of males [Fn(Z=-2.192, P=0.028), G0n(Z=-7.898, P<0.001), G1n (Z=-2.343, P=0.019), G2n (Z=-8.414, P<0.001)], but FtotalS1/FtotalS2(Z=-5.049, P<0.001) and FS1/FS2 (Z=-3.336, P=0.001) of females were higher than those of males, indicating a higher saliva acidification level in males than in females. Conclusion: IgG N-glycosylation levels might be gender dependent.

Pediatric rehabilitation with the reachMAN's modular handle.

This paper presents the results of a preliminary study with one cerebral palsy child using the ReHaptic Handle, a novel robotic device for the pediatric rehabilitation of upper limb function. The device has two degrees-of-freedom to train pinching, forearm supination/pronation and wrist flexion/extension movements. Interactive computer games have been implemented to increase subjects' participation and engagement, thus promoting motor recovery. Pinching, with the index finger and thumb, forearm supination/pronation as well as wrist flexion/extension were trained two or three times a week for 15 minutes each. An increase in forearm supination/pronation movement precision and smoothness was observed with the subject, as well as a reduction in the movement duration.

Robust adaptive neural network control of uncertain nonholonomic systems with strong nonlinear drifts.

In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x0 (t0) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.

Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback.

This paper focuses on adaptive control of nonaffine nonlinear systems with zero dynamics using multilayer neural networks. Through neural network approximation, state feedback control is firstly investigated for nonaffine single-input-single-output (SISO) systems. By using a high gain observer to reconstruct the system states, an extension is made to output feedback neural-network control of nonaffine systems, whose states and time derivatives of the output are unavailable. It is shown that output tracking errors converge to adjustable neighborhoods of the origin for both state feedback and output feedback control.

Direct adaptive NN control of a class of nonlinear systems.

In this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing a special property of the affine term, the developed scheme,avoids the controller singularity problem completely. All the signals in the closed loop are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.

Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems.

This paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks, while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions." With the utilization of iterative Lyapunov design and neural networks, the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied. The design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. Leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural-network approximation errors. It is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals.. The control performance can be guaranteed by an appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach.

Adaptive neural network control of nonlinear systems by state and output feedback.

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.