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.     

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.     

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     

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.     

Linearity Analysis of Concurrent Logic Programs

Automatic memory management and the hiding of the notion of pointers are the prominent features of symbolic processing languages. They make programming easy and guarantee the safety of memory references. For the memory management of linked data structures, copying garbage collection is most widely used because of its simplicity and desirable properties. However, if certain properties about runtime storage allocation and the behavior of pointers can be obtaind by static analysis, a compiler may be able to generate object code closer to that of procedural programs. In the fields of parallel, distributed and real-time computation, it is highly desirable to be able to identify data structures in a program that can be managed without using garbage collection. To this end, this paper proposes a framework of linearity analysis for a concurrent logic language Moded Flat GHC, and proves its basic property. The purpose of linearity analysis is to distinguish between fragments of data structures that may be referenced by two or more pointers and those that cannot be referenced by two or more pointers. Data structures with only one reader are amenable to compile-time garbage collection or local reuse. The proposed framework of linearity analysis is constraint-based and involves both equality and implicational constraints. It has been implemented as part of klint v2, a static analyzer for KL1 programs.     

Wide-gap layered oxychalcogenide semiconductors: Materials, electronic structures and optoelectronic properties

Applying the concept of materials design for transparent conductive oxides to layered oxychalcogenides, several p-type and n-type layered oxychalcogenides were proposed as wide-gap semiconductors and their basic optical and electrical properties were examined. The layered oxychalcogenides are composed of ionic oxide layers and covalent chalcogenide layers, which bring wide-gap and conductive properties to these materials, respectively. The electronic structures of the materials were examined by normal/inverse photoemission spectroscopy and energy band calculations. The results of the examinations suggested that these materials possess unique features more than simple wide-gap semiconductors. Namely, the layered oxychalcogenides are considered to be extremely thin quantum wells composed of the oxide and chalcogenide layers or 2D chalcogenide crystals/molecules embedded in an oxide matrix. Observation of step-like absorption edges, large band gap energy and large exciton binding energy demonstrated these features originating from 2D density of states and quantum size effects in these layered materials.