Hydrothermal synthesis and characterization of bis(4,4 `-bipyridinium) dodecatungstosilicate dihydrate

Hydrothermal synthesis and structural characterization of a new organic polyoxometalate, namely, bis(4,4'-bipyridinium) dodecatungstosilicate dihydrate, 4,4'-bipyH(2)](2)SiW12O40]center dot 2H(2)O (1) is reported. The crystal structure of (1) consists of a SiW12O40](4-) Keggin anion situated on a two-fold axis, two unique 4,4'-bipyridinium cations, one of which is situated on a two-fold axis, and two independent lattice water molecules. The cations and anions are linked by H-bonding.

Synthetic glutathione peroxidase mimics: effect of nucleophilicity of the aryl thiol cofactor on the antioxidant activity

Catalytic activity of a series of potent amide- and amine-based organoselenium compounds are studied in the presence of various aromatic thiols having electron donating and electron withdrawing substituents on the phenyl ring. This study suggests that the antioxidant activities of the synthetic GPx mimics can be significantly increased by the incorporation of a suitable electron donating group on the phenyl ring of an aromatic thiol.

Analysis of Wrist Pulse Signals Using Spatial Features in Time Domain

Wrist pulse signal contains more important information about the health status of a person and pulse signal diagnosis has been employed in oriental medicine since very long time. In this paper we have used signal processing techniques to extract information from wrist pulse signals. For this purpose we have acquired radial artery pulse signals at wrist position noninvasively for different cases of interest. The wrist pulse waveforms have been analyzed using spatial features. Results have been obtained for the case of wrist pulse signals recorded for several subjects before exercise and after exercise. It is shown that the spatial features show statistically significant changes for the two cases and hence they are effective in distinguishing the changes taking place due to exercise. Support vector machine classifier is used to classify between the groups, and a high classification accuracy of 99.71% is achieved. Thus this paper demonstrates the utility of the spatial features in studying wrist pulse signals obtained under various recording conditions. The ability of the model to distinguish changes occurring under two different recording conditions can be potentially used for health care applications.

Study of Wrist Pulse Signals Using a Bi-Modal Gaussian Model

Wrist pulse signals contain important information about the health of a person and hence diagnosis based on pulse signals has assumed great importance. In this paper we demonstrate the efficacy of a two term Gaussian model to extract information from pulse signals. Results have been obtained by conducting experiments on several subjects to record wrist pulse signals for the cases of before exercise and after exercise. Parameters have been extracted from the recorded signals using the model and a paired t-test is performed, which shows that the parameters are significantly different between the two groups. Further, a recursive cluster elimination based support vector machine is used to perform classification between the groups. An average classification accuracy of 99.46% is obtained, along with top classifiers. It is thus shown that the parameters of the Gaussian model show changes across groups and hence the model is effective in distinguishing the changes taking place due to the two different recording conditions. The study has potential applications in healthcare.