Mean Field Analysis of Stochastic Neural Network Models with Synaptic Depression

We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing state variable and a synaptic variable. In these equations, their average product is decoupled as the product of averaged them because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady state equations for a network with uniform connections and a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady state solutions. An oscillatory uniform state was observed in the network with uniform connections due to a Hopf instability. With the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previous unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.Comment: 26 pages, 13 figures. Preliminary results for the present work have been published elsewhere (Y Igarashi et al., 2009. http://www.iop.org/EJ/abstract/1742-6596/197/1/012018

Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models

Emergency responses during a massive tsunami disaster require information on the flow depth of land for rescue operations. This study aims to predict tsunami flow depth distribution in real time using regression and machine learning. Training data of 3480 earthquake-induced tsunamis in the Nankai Trough were constructed by numerical simulations. Initially, the k-means method was used to discriminate the areas with approximately the same flow depth. The number of clustered areas was 18, and the standard deviation of the flow depth data in a cluster was 0.46 m on average. The objective variables were the mean and standard deviation of the flow depth in the clustered areas. The explanatory variables were the maximum deviation of the water pressure at the seafloor observation points of the DONET observatory. We generated multiple regression equations for a power law using these datasets and the conjugate gradient method. Further, we employed the multilayer perceptron method, a machine learning technique, to evaluate the prediction performance. Both methods accurately predicted the tsunami flow depth calculated by testing 11 earthquake scenarios in the cabinet office of the government of Japan. The RMSE between the predicted and the true (via forward tsunami calculations) values of the mean flow depth ranged from 0.34–1.08 m. In addition to large-scale tsunami prediction systems, prediction methods with a robust and light computational load as used in this study are essential to prepare for unforeseen situations during large-scale earthquakes and tsunami disasters

A nonlinear parametric model based on a power law relationship for predicting the coastal tsunami height

When a subduction-zone earthquake occurs, the tsunami height must be predicted to cope with the damage generated by the tsunami. Therefore, tsunami height prediction methods have been studied using simulation data acquired by large-scale calculations. In this research, we consider the existence of a nonlinear power law relationship between the water pressure gauge data observed by the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the coastal tsunami height. Using this relationship, we propose a nonlinear parametric model and conduct a prediction experiment to compare the accuracy of the proposed method with those of previous methods and implement particular improvements to the extrapolation accuracy

Maximum tsunami height prediction using pressure gauge data by a Gaussian process at Owase in the Kii Peninsula, Japan

We constructed a model to predict the maximum tsunami height by a Gaussian process (GP) that uses pressure gauge data from the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) in the Nankai trough. We found a greatly improved generalization error of the maximum tsunami height by our prediction model. The error is about one third of that by a previous method, which tends to make larger predictions, especially for large tsunami heights (>10 m). These results indicate that GP enables us to get a more accurate prediction of tsunami height by using pressure gauge data

非一様な結合が神経回路の非線形ダイナミクスに及ぼす影響について

学位の種別:課程博士University of Tokyo(東京大学

Proteomic Profiling of Thyroid Papillary Carcinoma

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy. We performed shotgun liquid chromatography (LC)/tandem mass spectrometry (MS/MS) analysis on pooled protein extracts from patients with PTC and compared the results with those from normal thyroid tissue validated by real-time (RT) PCR and immunohistochemistry (IHC). We detected 524 types of protein in PTC and 432 in normal thyroid gland. Among these proteins, 145 were specific to PTC and 53 were specific to normal thyroid gland. We have also identified two important new markers, nephronectin (NPNT) and malectin (MLEC). Reproducibility was confirmed with several known markers, but the one of two new candidate markers such as MLEC did not show large variations in expression levels. Furthermore, IHC confirmed the overexpression of both those markers in PTCs compared with normal surrounding tissues. Our protein data suggest that NPNT and MLEC could be a characteristic marker for PTC

Enhancement activity of QS autoinducer analog

In this study, we have investigated the effects of the newly synthesized analog of Pseudomonas aeruginosa quorum-sensing autoinducer named AIA-1 (autoinducer analog) against antibiotic-resistant bacteria. In vitro susceptibility and killing assays for P. aeruginosa PAO1ΔoprD mutant and clinical isolates were performed by using antibiotics and AIA-1. In an in vivo assay, a luminescent carbapenem-resistant strain derived from PAO1ΔoprD was injected into neutropenic ICR mice and bioluminescence images were acquired after the treatment with antibiotics and AIA-1. Additionally, we investigated the effects of the combination use against carbapenem- resistant Enterobacteriaceae (CRE). Using killing assays in P. aeruginosa, the survival rates in the presence of antibiotics and AIA-1 significantly decreased in comparison with those with antibiotics alone. Furthermore, dual treatment of biapenem and AIA-1 was more effective than biapenem alone in a mouse infection model. AIA-1 did not change the MICs in P. aeruginosa, suggesting that AIA-1 acts on the mechanism of antibiotic tolerance. Conversely, the MICs of antibiotics decreased in the presence of AIA-1 in some CRE strains, indicating that AIA-1 may require additional mechanism to act on CRE. In conclusion, AIA-1 may be a potent drug for clinical treatment of infections caused by antibiotic-resistant bacteria

Conjugated docosahexaenoic acid suppresses KPL-1 human breast cancer cell growth in vitro and in vivo: potential mechanisms of action

Introduction The present study was conducted to examine the effect of conjugated docosahexaenoic acid (CDHA) on cell growth, cell cycle progression, mode of cell death, and expression of cell cycle regulatory and/or apoptosis-related proteins in KPL-1 human breast cancer cell line. This effect of CDHA was compared with that of docosahexaenoic acid (DHA). Methods KPL-1 cell growth was assessed by colorimetric 3- (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay; cell cycle progression and mode of cell death were examined by flow cytometry; and levels of expression of p53, p21Cip1/Waf1, cyclin D1, Bax, and Bcl-2 proteins were examined by Western blotting analysis. In vivo tumor growth was examined by injecting KPL-1 cells subcutaneously into the area of the right thoracic mammary fat pad of female athymic mice fed a CDHA diet. Results CDHA inhibited KPL-1 cells more effectively than did DHA (50% inhibitory concentration for 72 hours: 97 μmol/l and 270 μmol/l, respectively). With both CDHA and DHA growth inhibition was due to apoptosis, as indicated by the appearance of a sub-G1 fraction. The apoptosis cascade involved downregulation of Bcl-2 protein; Bax expression was unchanged. Cell cycle progression was due to G0/G1 arrest, which involved increased expression of p53 and p21Cip1/Waf1, and decreased expression of cyclin D1. CDHA modulated cell cycle regulatory proteins and apoptosis-related proteins in a manner similar to that of parent DHA. In the athymic mouse system 1.0% dietary CDHA, but not 0.2%, significantly suppressed growth of KPL-1 tumor cells; CDHA tended to decrease regional lymph node metastasis in a dose dependent manner. Conclusion CDHA inhibited growth of KPL-1 human breast cancer cells in vitro more effectively than did DHA. The mechanisms of action involved modulation of apoptosis cascade and cell cycle progression. Dietary CDHA at 1.0% suppressed KPL-1 cell growth in the athymic mouse system.</p