Melting domain size and recrystallization dynamics of ice revealed by time-resolved x-ray scattering.

The phase transition between water and ice is ubiquitous and one of the most important phenomena in nature. Here, we performed time-resolved x-ray scattering experiments capturing the melting and recrystallization dynamics of ice. The ultrafast heating of ice I is induced by an IR laser pulse and probed with an intense x-ray pulse which provided us with direct structural information on different length scales. From the wide-angle x-ray scattering (WAXS) patterns, the molten fraction, as well as the corresponding temperature at each delay, were determined. The small-angle x-ray scattering (SAXS) patterns, together with the information extracted from the WAXS analysis, provided the time-dependent change of the size and the number of liquid domains. The results show partial melting (~13%) and superheating of ice occurring at around 20 ns. After 100 ns, the average size of the liquid domains grows from about 2.5 nm to 4.5 nm by the coalescence of approximately six adjacent domains. Subsequently, we capture the recrystallization of the liquid domains, which occurs on microsecond timescales due to the cooling by heat dissipation and results to a decrease of the average liquid domain size.

Automatic detection of malaria parasite in blood images using two parameters.

Malaria must be diagnosed quickly and accurately at the initial infection stage and treated early to cure it properly. The malaria diagnosis method using a microscope requires much labor and time of a skilled expert and the diagnosis results vary greatly between individual diagnosticians. Therefore, to be able to measure the malaria parasite infection quickly and accurately, studies have been conducted for automated classification techniques using various parameters. In this study, by measuring classification technique performance according to changes of two parameters, the parameter values were determined that best distinguish normal from plasmodium-infected red blood cells. To reduce the stain deviation of the acquired images, a principal component analysis (PCA) grayscale conversion method was used, and as parameters, we used a malaria infected area and a threshold value used in binarization. The parameter values with the best classification performance were determined by selecting the value (72) corresponding to the lowest error rate on the basis of cell threshold value 128 for the malaria threshold value for detecting plasmodium-infected red blood cells.

Looking for optimized weights of CA125 and HE4 in early screening system of ovarian cancer for Korean patients.

We try to find out the best weight values of CA125 and HE4 in a discriminant formula classifying ovarian cancer patients from benign patients. We utilize a logistic regression analysis for the early screening system of the ovarian cancer for Korean patients. We compare our system with ROMA (Risk of Ovarian Malignancy Algorithm) of Abbot corp. In view of AUC (Area under the ROC curve), sensitivity with 95% of specificity and accuracy are considered. We performed experiments based on the logistic regression analysis separated by the case of pre- and post- menopausal stages and by the stages of progression of cancer. In our experiments, we can increase about 15.6% points of sensitivity with 95% of specificity, compared to that of ROMA. In premenopausal cases, ROMA shows 93.32% of AUC value and our system shows 97.48% of AUC, 4.1% points higher than ROMA. AUC of the ROMA for premenopausal women was 93.32%, whereas the AUC of our system was 97.48%. Furthermore, the AUC of the ROMA for early-staged ovarian cancer was 91.35%, whereas the AUC of our system was 97.22%, showing that the diagnostic performance of our system was superior over that of the ROMA in Korean patient cases.