Clinical application of single-shot echo-planar diffusion-weighted imaging with compressed SENSE in prostate MRI at 3T: preliminary experience

Magnetic Resonance Materials in Physics, Biology and Medicine - Image quality (IQ) of diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) suffers from low signal-to-noise...     

The effect of small dose of topiroxostat on serum uric acid in patients receiving hemodialysis

Introduction : Topiroxostat, a recently developed xanthine oxidase inhibitor, is expected to have fewer adverse effects than allopurinol because it has different mechanism of action from alloprinol. However, its dosage, usage and safety have not been established in patients with impaired renal function or those undergoing dialysis at the development since no studies was conducted in these patients. Methods : Cross over clinical trial using 3 months of allopurinol and topiroxostat on 27 maintain Japanese HD patients were carried out. The effects on oxidative stress status of both drugs were also evaluated by measuring oxidation reduction potential. Findings : Twenty‐five of twenty‐seven patients completed study. The mean serum uric acid levels in the topiroxostat‐treated arm was significantly lower than it in the allopurinol‐treated arm time‐dependently (P < 0.0001). Corrected oxidative stress ratio defined as biological antioxidant potential/diacron reactive oxygen metabolites was significantly increased in topiroxostat‐arm (*P = 0.0035), but not in allopurinol‐arm (P = 0.1429). No significant difference was seen in diacron reactive oxygen metabolites, biological antioxidant potential, static oxidation‐reduction potential, and capacity oxidation‐reduction potential between pre and post treatment of both drugs. Discussion : It is suggested that a low dose of topiroxostat decreased serum uric acid sufficiently to maintain it below 7.0 mg/dL in patients receiving hemodialysis.     

Template‐Free Fabrication of Mesoporous Alumina Nanospheres Using Post‐Synthesis Water‐Ethanol Treatment of Monodispersed Aluminium Glycerate Nanospheres for Molybdenum Adsorption

This work reports the template‐free fabrication of mesoporous Al₂O₃ nanospheres with greatly enhanced textural characteristics through a newly developed post‐synthesis “water‐ethanol” treatment of aluminium glycerate nanospheres followed by high temperature calcination. The proposed “water‐ethanol” treatment is highly advantageous as the resulting mesoporous Al₂O₃ nanospheres exhibit 2–4 times higher surface area (up to 251 m² g^?1), narrower pore size distribution, and significantly lower crystallization temperature than those obtained without any post‐synthesis treatment. To demonstrate the generality of the proposed strategy, a nearly identical post‐synthesis “water treatment” method is successfully used to prepare mesoporous monometallic (e.g., manganese oxide (MnO₂)) and bimetallic oxide (e.g., CuCo₂O₄ and MnCo₂O₄) nanospheres assembled of nanosheets or nanoplates with highly enhanced textural characteristics from the corresponding monometallic and bimetallic glycerate nanospheres, respectively. When evaluated as molybdenum (Mo) adsorbents for potential use in molybdenum‐99/technetium‐99m (⁹⁹Mo/^99mTc) generators, the treated mesoporous Al₂O₃ nanospheres display higher molybdenum adsorption performance than non‐treated Al₂O₃ nanospheres and commercial Al₂O₃, thereby suggesting the effectiveness of the proposed strategy for improving the functional performance of oxide materials. It is expected that the proposed method can be utilized to prepare other mesoporous metal oxides with enhanced textural characteristics and functional performance.     

LMNtal as a hierarchical logic programming language

LMNtal (pronounced “elemental”) is a simple language model based on hierarchical graph rewriting that uses logical variables to represent connectivity and membranes to represent hierarchy. LMNtal is an outcome of the attempt to unify constraint-based concurrency and Constraint Handling Rules (CHR), the two notable extensions to concurrent logic programming. LMNtal is intended to be a substrate language of various computational models, especially those addressing concurrency, mobility and multiset rewriting. Although the principal objective of LMNtal was to provide a unifying computational model, it is of interest to equip the formalism with a precise logical interpretation. In this paper, we show that it is possible to give LMNtal a simple logical interpretation based on intuitionistic linear logic and a flattening technique. This enables us to call LMNtal a hierarchical, concurrent linear logic language.     

A cross-junction channel valveless-micropump with PZT actuation

A gas-jet micro pump with novel cross-junction channel has been designed and fabricated using a Si micromachining process. The valveless micro pump is composed of a piezoelectric lead zirconate titanate (PZT) diaphragm actuator and fluidic network. The design of the valveless pump focuses on a cross-junction formed by the neck of the pump chamber and one outlet and two opposite inlet channels. The structure of cross-junction allows differences in fluidic resistance and fluidic momentum inside the channels during each PZT diaphragm vibration cycle, which leads to the gas flow being rectified without valves. The flow channels were easily fabricated by using silicon etching process. To investigate the effects of the structure of the cross-junction on the gas flow rate, two types of pump with different cross-junction were studied. The design and simulation were done using ANSYS-Fluent software. The simulations and experimental data revealed that the step-nozzle structure is much more advantageous than the planar structure. A flow rate of 5.2 ml/min was obtained for the pump with step structure when the pump was driven at its resonant frequency of 7.9 kHz by a sinusoidal voltage of 50 V_p–p.     

Optimal linear combination of neural networks for improvingclassification performance

This paper presents a new method for linearly combining multiple neural network classifiers based on the statistical pattern recognition theory. In our approach, several neural networks are first selected based on which works best for each class in terms of minimizing classification errors. Then, they are linearly combined to form an ideal classifier that exploits the strengths of the individual classifiers. In this approach, the minimum classification error criterion is utilized to estimate the optimal linear weights. In this formulation, because the classification decision rule is incorporated into the cost function, a more suitable better combination of weights for the classification objective could be obtained. Experimental results using artificial and real data sets show that the proposed method can construct a better combined classifier that outperforms the best single classifier in terms of overall classification errors for test data     

View-based virtual learning and recognition of 3D object using view model obtained by motion-stereo

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning approach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features of high-order autocorrelation are extracted, and discriminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effectiveness of the proposed method. Caihua Wang, Ph.D.: He received his B.S. in mathematics and M.E. in electronic engineering from Renmin University of China, Beijing, China in 1983 and 1986, and his Ph. D. from Shizuoka University, Hamamatsu, Japan in 1996. He is a JST domestic fellow and is doing his post doctoral research at Electrotechnical Laboratory. His research interests are computer vision and image processing. He is a member of IEICE and IPSJ. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information.     

Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.     

Parametric embedding for class visualization

We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.