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Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV)., Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation., Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively., Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.

Appropriate input variables have to be selected for building highly accurate soft sensor. A novel input variable selection method based on nearest correlation spectral clustering (NCSC) has been proposed, and it is referred to as NCSC-based variable selection (NCSC-VS). Although NCSC-VS can select appropriate input variables, a lot of parameters have to be tuned carefully for selecting proper variables. The present work proposes a new methodology for efficient input variable selection by integrating NCSC and group Lasso. The proposed NCSC-based group Lasso (NCSC-GL) can not only reduce the number of tuning parameters but also achieve almost the same performance as NCSC-VS. The usefulness of the proposed NCSC-GL is demonstrated through applications to soft sensor design for a pharmaceutical process and a chemical process., (Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.)

Although sleep apnea syndrome (SAS) is a common sleep disorder, most patients with sleep apnea are undiagnosed and untreated because it is difficult for patients themselves to notice SAS in daily living. Polysomnography (PSG) is a gold standard test for sleep disorder diagnosis, however PSG cannot be performed in many hospitals. This fact motivates us to develop an SAS screening system that can be used easily at home. The autonomic nervous function of a patient changes during apnea. Since changes in the autonomic nervous function affect fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), SAS can be detected through monitoring HRV. The present work proposes a new HRV-based SAS screening algorithm by utilizing support vector machine (SVM), which is a well-known pattern recognition method. In the proposed algorithm, various HRV features are derived from RRI data in both apnea and normal respiration periods of patients and healthy people, and an apnea/normal respiration (A/N) discriminant model is built from the derived HRV features by SVM. The result of applying the proposed SAS screening algorithm to clinical data demonstrates that it can discriminate patients with sleep apnea and healthy people appropriately. The sensitivity and the specificity of the proposed algorithm were 100% and 86%, respectively.

The planar and stereostructures of JBIR-108 isolated from Streptomyces gramineus IR087Pi-4 were determined partly by spectral analysis, and these structural assignments were confirmed and completed by the total synthesis of both 1-epimers. The key stereocenters in JBIR-108 were constructed via a Corey-Bakshi-Shibata (CBS) reduction (C-1), vinylogous Mukaiyama aldol reaction (C-7), and Brown crotylation (C-14 and C-15). Although it was difficult to determine the stereochemistries at the C-1 and C-7 positions in the natural product using the modified Mosher's method, the synthesis of two possible C-1 diastereomers enabled the identification of the configurations at the hitherto unknown stereocenters.

Scanning tunneling microscopy (STM) and low-energy electron diffraction were used to reveal the structures of ordered adlayers of [2+2]-type C60-C60 fullerene dimer (C120) and C60-C70 cross-dimer (C130) formed on Au(111) by immersingit in abenzene solution containing C120 or C130 molecules. High-resolution STM images clearly showed the packing arrangements and the electronic structures of C120 and C130 on the Au(111) surface in ultrahigh vacuum. The (2 square root3 x 4square root3)R30 degrees, (2square root3 x 5square root3)R30 degrees, and (7 x 7) structures were found for the C120 adlayer on the Au(111) surface, whereas C130 molecules were closely packed on the surface. Each C60 or C70 monomer cage was discerned in the STM image of a C130 molecule.

[reaction: see text] A novel C(60) dimer connected by a silicon bridge and a single bond was synthesized by the mechanochemical solid-state reaction employing a high-speed vibration milling technique and fully characterized by the (1)H and (13)C NMR, APCI mass, and UV-vis spectroscopy. The presence of the electronic interaction between the two C(60) cages was demonstrated by the electrochemical method.

The solid-state reaction using a high-speed vibration milling (HSVM) technique has been applied to the [4 + 2] cycloaddition of fullerene C(60) with condensed aromatics such as anthracenes, tetracene, pentacene, and naphtho[2,3-a]pyrene. From the reaction with anthracene, [4 + 2] cycloadduct 1 was obtained in better yield than the reaction in solution. Despite such heterogeneous solid-state reaction conditions, an equilibrium state appears to be attained between the reactants (C(60) and anthracene) and products (mono- and bisadducts) since essentially the same product mixture was obtained starting either from the reactants or from the monoadduct after 30 min of the HSVM reaction. The reaction with pentacene gave double C(60) adduct 4 with two C(60) cages attached to a pentacene molecule, which cannot be obtained in the reaction in toluene solution, in addition to symmetrical monoadduct 3. The HSVM treatment of C(60) with 9,10-dimethylanthracene, tetracene, or naphtho[2,3-a]pyrene also gave the corresponding [4 + 2] cycloadducts in fair yields. The redox properties of these adducts were investigated by the use of cyclic voltammetry.