Effects of intravenous lansoprazole on acute gastric mucosal lesions and acid secretion

The effects of lansoprazole given intravenously on gastric mucosal lesions, gastric bleeding and acid secretion were investigated in rats in comparison with those of omeprazole, famotidine and ranitidine. Lansoprazole inhibited the formation of gastric mucosal lesions in rats induced by water-immersion stress or aspirin with ID50 values of 0.26 and 0.99 mg/kg, respectively, and also inhibited gastric bleeding induced by hemorrhagic shock or water-immersion stress with ID50 values of 0.46 and 1.22 mg/kg, respectively. Lansoprazole was more potent than omeprazole, famotidine and ranitidine in inhibiting gastric mucosal lesions and hemorrhagic shock- or stress-induced bleeding. Famotidine and ranitidine showed negligible inhibition of water-immersion stress-induced gastric bleeding. Lansoprazole strongly inhibited water-immersion stress-stimulated acid secretion in rats, whereas famotidine and ranitidine did not show a potent inhibitory effect. These results indicate that lansoprazole exerts prominent inhibitory actions against the formation of gastric mucosal lesions and gastric bleeding by inhibiting acid secretion, and they show that it is superior to histamine H2-receptor antagonists in inhibiting stress-induced gastric bleeding.     

Software‐processed edge‐ and level‐adaptive overdrive (SELAO) method for high‐quality motion pictures

Abstract— In this paper, a software‐processed edge‐ and level‐adaptive overdrive (SELAO) method, which is a novel overdrive technique that utilizes not only a temporal change of gray levels but also a spatial edge intensity of motion pictures, is proposed. The SELAO method is a software video‐processing technology to improve motion‐picture quality rendered on LCDs more than is possible with a conventional SLAO method without edge‐adaptive overdrive, and it works in real time on commonly used personal computers (PCs).     

Analysis of polarization plane rotation characteristic in 2D photonic crystal waveguide with chiral medium by condensed node spatial network

Using the photonic band gap in photonic crystals, the fundamental waveguide structures for the light wavelength range have been developed. Based on the fine structure of these many functional devices have been proposed by analytical or numerical simulation methods and the experiments of trial manufacture. In this paper, the treatment of chiral dielectric in the Condensed Node Spatial Network for the vector potential is explained, and we show the polarization plane rotation property in air‐hole and pillar type photonic crystal waveguide structures with the chiral medium substrate. Then, we show the fundamental advantage of the air‐hole type photonic crystal waveguide structure in application to a mode converter. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(1): 7–14, 2005; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20098     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

High oleic acid oil suppresses lung tumorigenesis in mice through the modulation of extracellular signal-regulated kinase cascade

This study was undertaken to estimate the effect of dietary high oleic acid oil (OA) on 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)-induced lung tumorigenesis in mice. Diet containing 10% oil was fed to mice through experimental periods. On day 30 after NNK injection (100 mg/kg body weight, i.p.), the treatment increased the level of prostaglandin E₂ (PGE₂) as well as proliferating cell nuclear antigen, a marker of cell proliferation in a high linoleic acid oil (LA)-fed group but not in an OA-fed group. The NNK treatment also induced the activation of an extracellular signal-regulated kinase (Erk) cascade (Erk, Mek and Raf-1) in an LA-fed group. On the other hand, OA feeding abolished the NNK-induced activation of the Erk cascade. In conjugation with these events, OA feeding reduced lung tumor incidence and tumor multiplicity (percentage of mice with tumors) in mice compared with LA feeding at the 20th experimental week. These results suggest that OA suppresses lung tumorigenesis and that this suppression is correlated with the inhibition of PGE₂ production and inactivation of the Erk cascade.     

Dehydrogenative Cracking of n-Butane over Modified HZSM-5 Catalysts

Dehydrogenative cracking reaction of n-butane was studied using HZSM-5 catalyst modified with various metal oxides. Alkaline earth (magnesium), transition metal (cobalt) and rare earth (lanthanum) elements are used for the modification. The selectivity of the products was studied at low conversion (20%). Methane, ethane, ethylene, propylene, butenes and butadiene were the main products. With the use of the cobalt- or magnesium-containing HZSM-5, dehydrogenative cracking was observed and the selectivity of ethylene was much larger than that of ethane. On the other hand, the selectivity of ethylene and ethane were almost the same in the reaction using the lanthanum-containing HZSM-5. It is considered that the cobalt- and magnesium-loaded sites on HZSM-5 played an important role in the dehydrogenative cracking.     

Stochastic Resonance in Recurrent Neural Network with Hopfield-Type Memory

Stochastic resonance (SR) is known as a phenomenon in which the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. In this paper, we investigate how SR behavior can be observed in practical autoassociative neural networks with the Hopfield-type memory under the stochastic dynamics. We focus on SR responses in two systems which consist of three and 156 neurons. These cases are considered as effective double-well and multi-well models. It is demonstrated that the neural network can enhance weak subthreshold signals composed of the stored pattern trains and have higher coherence abilities between stimulus and response.     

Microstructure and properties of (Sr, Ca)CuO2/(Sr, Ca)RuO3 multilayers

Epitaxial multilayer thin films of infinite-layer (Sr, Ca)CuO₂ and perovskite (Sr, Ca)RuO₃ have been prepared on (100) SrTiO₃ substrates by multitarget rf magnetron sputtering. X-ray diffraction analyses revealed that the multilayer structure of (Sr, Ca)CuO₃/(Sr, Ca)RuO₃ was successfully fabricated with a minimum layer thickness of 20 Å. Transmission electron microscopy measurements of the multilayers indicated that there was no dislocation which normally exists in single-layer films with an infinite-layer structure. Resistivities of multilayer films at room temperature ranged from 1 to 10 m cm and showed semiconductor-like dependence against the temperature.     

Evidence for a Jahn-Teller distortion of Cs2NaHoCl6 at 150 mK observed by X-ray diffractometry

By means of SR X-ray diffractometry, cooperative Jahn-Teller distortion as a structural phase transformation of the first kind is directly confirmed by a splitting of Laue spots. It is understood that the degenerate ground state of a nonmagnetic doublet (₃) of Ho³⁺ in a cubic Ho-elpasolite, Fm3m(O _h ⁵ ), at least above 600 mK, is lifted up by a distortion below 150 mK, which was predicted by a splitting of the NQR spectrum and anomalies in the specific heat and magnetic susceptibility. This structural transformation shows not only the lowest transition temperature up to now, but also the greatest hysteresis specified by a Schottky-type excitation. The magnitude of the relative distortion (1/1) has been roughly estimated to be on the 2.0 · 10^–3 order of magnitude, which is much larger than the predicted value obtained from a point charge approximation. The pecularities of this transition are discussed from the viewpoint that the general theory of a structural transformation, established convincingly at high temperatures could not be easily applied.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.