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Water Hydrogen Bonds Study by Opto-Magnetic Fingerprint Technique

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Koruga Đ., Miljković S., Ribar S., Matija L., Kojić D.

issue 5

777-781

Hydrogen bond has dual property, classical (electrostatic interaction based on Coulomb's law) and quantum (wave function based on Schrödinger equation). Since Planck's constant is one of the main criteria for decision which process is quantum, or how much is close to be quantum, we use electrical and magnetic forces of valence electrons, as point of departure, to develop method for opto-magnetic fingerprint of matter. During the study of different type of matter we observed phenomena from spectral convolution data of digital images which characterize matter from both covalent and non-covalent bonding. Since water is matter that is most abundant with hydrogen bonds, we present results of 18.2 MΩ water investigation on different temperature and under influence of constant and variable magnetic fields by opto-magnetic method. Bearing in mind that Linus Pauling, in his book Nature of the Chemical Bond (Cornel University Press, 1939), for the first time presented the systematic concept of the hydrogen bond to the molecular world and its machinery, this paper is written in honor to him and 70th anniversary of one of the most important scientific paradigm.

Optical Biopsy Method for Breast Cancer Diagnosis Based on Artificial Neural Network Classification οf Fluorescence Landscape Data

100%

Dramićanin T., Zeković I., Dimitrijević B., Ribar S., Dramićanin M.

Acta Physica Polonica A

2009

vol. 116

issue 4

690-692

Supervised self-organizing map, a type of artificial neural network, is applied for classification of human breast tissue samples utilizing data obtained from fluorescence landscape measurements. Female breast tissue samples were taken soon after the surgical resection, identified and stored at -80°C until fluorescence measurements. From fluorescence landscapes obtained in UV-VIS region spectral features showing statistically significant differences between malignant and normal samples are identified and further quantified to serve as a training input to neural network. Additional set of samples was used as a test group input to trained network in order to evaluate performance of proposed optical biopsy method. Classification sensitivity of 83.9% and specificity of 88.9% are found.

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2 Acta Physica Polonica A

1 2010

1 2009

2 Ribar S.

1 Dimitrijević B.

1 Dramićanin M.

1 Dramićanin T.

1 Kojić D.

1 Koruga Đ.

1 Matija L.

1 Miljković S.

1 Zeković I.

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Water Hydrogen Bonds Study by Opto-Magnetic Fingerprint Technique

Optical Biopsy Method for Breast Cancer Diagnosis Based on Artificial Neural Network Classification οf Fluorescence Landscape Data