[Advances in the regulation of lncRNA on laryngeal squamous cell carcinoma].

Laryngeal cancer (LC) is one of the most common malignant tumors that occur in the head and neck. Emerging evidence shows that coding RNAs and non-coding RNAs play key roles in the formation and progression of LC. In this review, we focus on the regulation of lncRNAs in LC. LncRNAs appear to be involved in laryngeal cancer growth, invasion, and metastasis and in establishment of the laryngeal tumor microenvironment through various mechanisms. Furthermore, we also discuss the possibilities of establishing lncRNAs as potential biomarkers and therapeutic targets for laryngeal cancer. Taken together, we summarize the emerging roles of lncRNAs in laryngeal cancer development and their possible clinical significance.

Sex- and afferent-specific differences in histamine receptor expression in vagal afferents of rats: A potential mechanism for sexual dimorphism in prevalence and severity of asthma.

The incidence of asthma is more common in boys than in girls during the childhood, and more common in premenopausal female than age-matched males. Our previous study demonstrated a gender difference in histamine-mediated neuroexcitability in nodose ganglia neurons (NGNs), highlighting a possibility of histamine-mediated gender difference in asthma via visceral afferent function. In the present study, we aimed to explore the gender difference in expression profiles of histamine receptors (HRs) in nodose ganglia (NG) and individual identified NGNs to provide deeper insights into the mechanisms involved in sexual dimorphism of asthma. Western-blot and SYBR green RT-PCR showed that H2R and H3R were highly expressed in NG of females compared with males and downregulated in ovariectomized females. H1R was equally expressed in NG of both sexes and not altered by ovariectomy. Furthermore, this highly expressive H2R and H3R were distributed in both myelinated and unmyelinated NGNs isolated from adult female rats by immunofluorescence and single-cell RT-PCR. H3R widely distributed in all tested neuron subtypes and its expression did not show significant difference among neuron subtypes. H2R was widely and highly expressed in low-threshold and sex-specific subpopulation of myelinated Ah-types compared with myelinated A- and unmyelinated C-type NGNs. Unexpectedly, weak expression of H1R was detected in both myelinated and unmyelinated NGNs by immunofluorescence, which was further confirmed by single-cell RT-PCR. Our results suggest that the sexual dimorphism in the expression of H2R and H3R in vagal afferents very likely contributes, at least partially, to the gender difference in prevalence and severity of asthma.

Global exponential stability of neural networks with globally Lipschitz continuous activations and its application to linear variational inequality problem.

This paper investigates the existence, uniqueness, and global exponential stability (GES) of the equilibrium point for a large class of neural networks with globally Lipschitz continuous activations including the widely used sigmoidal activations and the piecewise linear activations. The provided sufficient condition for GES is mild and some conditions easily examined in practice are also presented. The GES of neural networks in the case of locally Lipschitz continuous activations is also obtained under an appropriate condition. The analysis results given in the paper extend substantially the existing relevant stability results in the literature, and therefore expand significantly the application range of neural networks in solving optimization problems. As a demonstration, we apply the obtained analysis results to the design of a recurrent neural network (RNN) for solving the linear variational inequality problem (VIP) defined on any nonempty and closed box set, which includes the box constrained quadratic programming and the linear complementarity problem as the special cases. It can be inferred that the linear VIP has a unique solution for the class of Lyapunov diagonally stable matrices, and that the synthesized RNN is globally exponentially convergent to the unique solution. Some illustrative simulation examples are also given.

A complete proof of global exponential convergence of a neural network for quadratic optimization with bound constraints.

Sudharsanan and Sundareshan developed (1991) a neural-network model for bound constrained quadratic minimization and proved the global exponential convergence of their proposed neural network. The global exponential convergence is a critical property of the synthesized neural network for solving the optimization problem successfully. However, Davis and Pattison (1992) presented a counterexample to show that the proof given by Sudharsanan and Sundareshan for the global exponential convergence of the neural network is not correct. Bouzerdoum and Pattison (ibid., vol.4, no.2, p.293-303, 1993) then generalized the neural-network model given by Sudharsanan and Sundareshan and derived the global exponential convergence of the neural network under an appropriate condition. In this letter, we demonstrate through an example that the global exponential convergence condition given by Bouzerdoum and Pattison is not always satisfied by the quadratic minimization problem and show that the neural-network model under the global exponential convergence condition given by Bouzerdoum and Pattison is essentially restricted to contractive networks. Subsequently, a complete proof of the global exponential convergence of the neural-network models proposed by Sudharsanan and Sundareshan and Bouzerdoum and Pattison is given for the general case, without resorting to the global exponential convergence condition given by Bouzerdoum and Pattison. An illustrative simulation example is also presented.

A recurrent neural network for nonlinear continuously differentiable optimization over a compact convex subset.

We propose a general recurrent neural-network (RNN) model for nonlinear optimization over a nonempty compact convex subset which includes the bound subset and spheroid subset as special cases. It is shown that the compact convex subset is a positive invariant and attractive set of the RNN system and that all the network trajectories starting from the compact convex subset converge to the equilibrium set of the RNN system. The above equilibrium set of the RNN system coincides with the optimum set of the minimization problem over the compact convex subset when the objective function is convex. The analysis of these qualitative properties for the RNN model is conducted by employing the properties of the projection operator of Euclidean space onto the general nonempty closed convex subset. A numerical simulation example is also given to illustrate the qualitative properties of the proposed general RNN model for solving an optimization problem over various compact convex subsets.

Qualitative analysis of a recurrent neural network for nonlinear continuously differentiable convex minimization over a nonempty closed convex subset.

We investigate the qualitative properties of a recurrent neural network (RNN) for minimizing a nonlinear continuously differentiable and convex objective function over any given nonempty, closed, and convex subset which may be bounded or unbounded, by exploiting some key inequalities in mathematical programming. The global existence and boundedness of the solution of the RNN are proved when the objective function is convex and has a nonempty constrained minimum set. Under the same assumption, the RNN is shown to be globally convergent in the sense that every trajectory of the RNN converges to some equilibrium point of the RNN. If the objective function itself is uniformly convex and its gradient vector is a locally Lipschitz continuous mapping, then the RNN is globally exponentially convergent in the sense that every trajectory of the RNN converges to the unique equilibrium point of the RNN exponentially. These qualitative properties of the RNN render the network model well suitable for solving the convex minimization over any given nonempty, closed, and convex subset, no matter whether the given constrained subset is bounded or not.

Equivalence between local exponential stability of the unique equilibrium point and global stability for Hopfield-type neural networks with two neurons.

In a recent paper, Fang and Kincaid proposed an open problem about the relationship between the local stability of the unique equilibrium point and the global stability for a Hopfield-type neural network with continuously differentiable and monotonically increasing activation functions. As a partial answer to the question, in the two-neuron case it is proved that for each given specific interconnection weight matrix, a Hopfield-type neural network has a unique equilibrium point which is also locally exponentially stable for any activation functions and for any other network parameters if and only if the network is globally asymptotically stable for any activation functions and for any other network parameters. If the derivatives of the activation functions of the network are bounded, then the network is globally exponentially stable for any activation functions and for any other network parameters.

A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints.

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the neural network. If the objective function to be minimized is convex, then the recurrent neural network is complete in the sense that the set of optima of the function with bound constraints coincides with the set of equilibria of the neural network. 2) The recurrent neural network is primal and quasiconvergent in the sense that its trajectory cannot escape from the feasible region and will converge to the set of equilibria of the neural network for any initial point in the feasible bound region. 3) The recurrent neural network has an attractivity property in the sense that its trajectory will eventually converge to the feasible region for any initial states even at outside of the bounded feasible region. 4) For minimizing any strictly convex quadratic objective function subject to bound constraints, the recurrent neural network is globally exponentially stable for almost any positive network parameters. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.

[Complications of perineal artificial anus reconstruction after Mile's procedure and their management].

From 1987 to 1988, 122 patients with rectal carcinoma underwent Mile's procedure with their artificial anus reconstructed at the perineal region. In 59 patients (48.36%), complications occurred including wound infection on the perineum, mucosa prolapse of the artificial anus, anus retraction or severe edema, and perineal erosive dermatitis. The causes of the complications and the therapeutic approach were discussed.