Statistical Assessement on Cancer Risks of Ionizing Radiation and Smoking Based on Poisson Models

In many epidemiological and medical studies, a number of cancer motralities in catagorical classification may be considered as having Poisson distribution with person-years at risk depending upon time. The cancer mortalities have been evaluated by additive or multiplicative models with regard to background and excess risks based on several covariances such as sex, age at the time of bombings, time at exposure, or ionizing radiation, cigarette smoking habits, duration of smoking habits, etc. An interest herein to examine an additive, synergistic,or antagonistic relationship between radiation exposures and cigarette smoking habits for cancer mortalities. The results revealed a highly significant antagonistic influence for cancer mortalities from all nonhematologic findings, lung and respiratory system with negative interaction between radiation exposures and cigarette smoking amounts

Hadronic decays of B→a1(1260)b1(1235)B \to a_1(1260) b_1(1235) in the perturbative QCD approach

We calculate the branching ratios and polarization fractions of the $B \to a_1 b_1$ decays in the perturbative QCD(pQCD) approach at leading order, where $a_1$($b_1$) stands for the axial-vector $a_1(1260)[b_1(1235)]$ state. By combining the phenomenological analyses with the perturbative calculations, we find the following results: (a) the large decay rates around $10^{-5}$ to $10^{-6}$ of the $B \to a_1 b_1$ decays dominated by the longitudinal polarization(except for the $B^+ \to b_1^+ a_1^0$ mode) are predicted and basically consistent with those in the QCD factorization(QCDF) within errors, which are expected to be tested by the Large Hadron Collider and Belle-II experiments. The large $B^0 \to a_1^0 b_1^0$ branching ratio could provide hints to help explore the mechanism of the color-suppressed decays. (b) the rather different QCD behaviors between the $a_1$ and $b_1$ mesons result in the destructive(constructive) contributions in the nonfactorizable spectator diagrams with $a_1(b_1)$ emission. Therefore, an interesting pattern of the branching ratios appears for the color-suppressed $B^0 \to a_1^0 a_1^0, a_1^0 b_1^0,$ and $b_1^0 b_1^0$ modes in the pQCD approach, $Br(B^0 \to b_1^0 b_1^0) > Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$, which is different from $Br(B^0 \to b_1^0 b_1^0) \sim Br(B^0 \to a_1^0 b_1^0) \gtrsim Br(B^0 \to a_1^0 a_1^0)$ in the QCDF and would be verified at future experiments. (c) the large naive factorization breaking effects are observed in these $B \to a_1 b_1$ decays. Specifically, the large nonfactorizable spectator(weak annihilation) amplitudes contribute to the $B^0 \to b_1^+ a_1^-(B^+ \to a_1^+ b_1^0\; {\rm and}\; B^+ \to b_1^+ a_1^0)$ mode(s), which demand confirmations via the precise measurements.Comment: 13 pages, 1 figure, 5 tables, revtex fil

A Statistical Classification on a Mixture Distribution of Intelligence Quotients and Severe Mental Retardation

Main content of this paper is to classify IQ individuals into two categories of normal and abnormal groups. It is too difficult to divide IQ individuals into two groups of normal IQ group and abnormal group because of sparse number of cases with mental retardation. Therefore, we examined a normality of 1673 IQ individuals, but a significant difference was noted for the IQ data. The lowest three mentally retarded cases of less than or equal to 59 IQ score were excluded, the IQ data then fitted to a normal distribution well. The critical value which minimizes the probability of classification is obtained on the basis on an approximate technique with regard to normality. An approximate probability of misclassification for individuals at random from mixture of two normal populations is 25.5%

Visualization of three-way comparisons of omics data

BACKGROUND: Density plot visualizations (also referred to as heat maps or color maps) are widely used in different fields including large-scale omics studies in biological sciences. However, the current color-codings limit the visualizations to single datasets or pairwise comparisons. RESULTS: We propose a color-coding approach for the representation of three-way comparisons. The approach is based on the HSB (hue, saturation, brightness) color model. The three compared values are assigned specific hue values from the circular hue range (e.g. red, green, and blue). The hue value representing the three-way comparison is calculated according to the distribution of three compared values. If two of the values are identical and one is different, the resulting hue is set to the characteristic hue of the differing value. If all three compared values are different, the resulting hue is selected from a color gradient running between the hues of the two most distant values (as measured by the absolute value of their difference) according to the relative position of the third value between the two. The saturation of the color representing the three-way comparison reflects the amplitude (or extent) of the numerical difference between the two most distant values according to a scale of interest. The brightness is set to a maximum value by default but can be used to encode additional information about the three-way comparison. CONCLUSION: We propose a novel color-coding approach for intuitive visualization of three-way comparisons of omics data