Adaptive evolution of MRGX2, a human sensory neuron specific gene involved in nociception

MRGX2, a G-protein-coupled receptor, is specifically expressed in the sensory neurons of the human peripheral nervous system and involved in nociception. Here, we studied DNA polymorphism patterns and evolution of the MRGX2 gene in world-wide human populations and the representative nonhuman primate species. Our results demonstrated that MRGX2 had undergone adaptive changes in the path of human evolution, which were likely caused by Darwinian positive selection. The patterns of DNA sequence polymorphisms in human populations showed an excess of derived substitutions, which against the expectation of neutral evolution, implying that the adaptive evolution of MRGX2 in humans was a relatively recent event. The reconstructed secondary structure of the human MRGX2 revealed that three of the four human-specific amino acid substitutions were located in the extra-cellular domains. Such critical substitutions may alter the interactions between MRGX2 protein and its ligand, thus, potentially led to adaptive changes of the pain-perception-related nervous system during human evolution. (c) 2005 Elsevier B.V. All rights reserved.

Islet-1 is required for ventral neuron survival in Xenopus

Islet-1 is a LIM domain transcription factor involved in several processes of embryonic development. Xenopus Islet-1 (Xisl-1) has been shown to be crucial for proper heart development. Here we show that Xisl-1 and Xisl-2 are differentially expressed in th

Telemetric recordings of single neuron activity and visual scenes in monkeys walking in an open field

This paper describes a portable recording system and methods for obtaining chronic recordings of single units and tracking rhesus monkey behavior in an open field. The integrated system consists of four major components: (1) microelectrode assembly; (2) h

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Application of multi-weighted neuron for iris recognition

In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

A more complex neuron in biomimetic pattern recognition

Biomimetic pattern recognition has been proposed for several years, but the discussion of its neuron was not very wide and deep. In this paper, we propose a new more complex neuron named M-neuron and give the application in the last part of the paper.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

Fucoidan protects against dopaminergic neuron death in vivo and in vitro

Parkinson's disease is a neurodegenerative disorder of uncertain pathogenesis characterized by a loss of dopaminergic neurons in substantia nigra pars compacta, and can be modeled by the neurotoxin 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP). Oxidative stress may contribute to MPTP- and Parkinson's disease-related neurodegeneration. Fucoidan is a sulfated polysaccharide extracted from brown seaweeds which possesses a wide variety of biological activities including potent antioxidative effects. Here we investigated the effect of fucoidan treatment on locomoter activities of animals, striatal dopamine and its metabolites and survival of nigral dopaminergic neurons in MPTP-induced animal model of Parkinsonism in C57/BL mice in vivo and on the neuronal damage induced by 1-methyl-4-phenylpyridinium (MPP+) in vitro, and to study the possible mechanisms. When administered prior to MPTP, fucoidan reduced behavioral deficits, increased striatal dopamine and its metabolites levels, reduced cell death, and led to a marked increase in tyrosine hydroxylase expression relative to mice treated with MPTP alone. Furthermore, we found that fucoidan inhibited MPTP-induced lipid peroxidation and reduction of antioxidant enzyme activity. In addition, pre-treatment with fucoidan significantly protected against MPP+-induced damage in MN9D cells. Taken together, these findings suggest that fucoidan has protective effect in MPTP-induced neurotoxicity in this model of Parkinson's disease via its antioxidative activity. (C) 2009 Elsevier B.V. All rights reserved.

Properties of the proton-evoked currents and their modulation by Ca2+ and Zn2+ in the acutely dissociated hippocampus CA1 neurons

The characterization of acid-sensing ion channel (ASIC)-like currents has been reported in hippocampal neurons in primary culture. However, it is suggested that the profile of expression of ASICs changes in culture. In this study, we investigated the properties of proton-activated current and its modulation by extracellular Ca2+ and Zn2+ in neurons acutely dissociated from the rat hippocampal CA1 using conventional whole-cell patch-clamp recording. A rapidly decaying inward current and membrane depolarization was induced by exogenous application of acidic solution. The current was sensitive to the extracellular proton with a response threshold of pH 7.0-6.8 and the pH(50) Of 6.1, the reversal potential close to the Na+ equilibrium potential. It had a characteristic of acid-sensing ion channels (ASICs) as demonstrated by its sensitivity to amiloride (IC50 = 19.6 +/- 2.1 muM). Either low [Ca2+](0) or high [Zn2+](0) increased the amplitude of the current. All these characteristics are consistent with a current mediated through a mixture of homomeric ASIC1a and heteromeric ASIC1a + 2a channels and closely replicate many of the characteristics that have been previously reported for hippocampal neurons cultured for a week or more, indicating that culture artifacts do not necessarily flaw the properties of ASICs. Interestingly, we found that high [Zn2+] (>10(-4) M) slowed the decay time constant of the ASIC-like current significantly in both acutely dissociated and cultured hippocampal neurons. In addition, the facilitating effects of low [Ca2+](0) and high [Zn2+](0) on the ASIC-like current were not additive. Since tissue acidosis, extracellular Zn elevation and/or Ca2+ reduction occur concurrently under some physiological and/or pathological conditions, the present observations suggest that hippocampal ASICs may offer a novel pharmacological target for therapeutic invention. (C) 2004 Elsevier B.V. All rights reserved.

Coupling between NMDA receptor and acid-sensing ion channel contributes to ischemic neuronal death

Acid-sensing ion channels (ASICs) composed of ASIC1a subunit exhibit a high Ca2+ permeability and play important roles in synaptic plasticity and acid-induced cell death. Here, we show that ischemia enhances ASIC currents through the phosphorylation at Ser478 and Ser479 of ASIC1a, leading to exacerbated ischemic cell death. The phosphorylation is catalyzed by Ca2+/calmodulin-dependent protein kinase II (CaMKII) activity, as a result of activation of NR2B-containing N-methyl-D-aspartate subtype of glutamate receptors (NMDARs) during ischemia. Furthermore, NR2B-specific antagonist, CaMKII inhibitor, or overexpression of mutated form of ASIC1a with Ser478 or Ser479 replaced by alanine (ASICla-S478A, ASIC1a-S479A) in cultured hippocampal neurons prevented ischemia-induced enhancement of ASIC currents, cytoplasmic Ca2+ elevation, as well as neuronal death. Thus, NMDAR-CaMKII cascade is functionally coupled to ASICs and contributes to acidotoxicity during ischemia. Specific blockade of NMDAR/CaMKII-ASIC coupling may reduce neuronal death after ischemia and other pathological conditions involving excessive glutamate release and acidosis.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.