GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and genetic variations of the breast cancer cells and helps in understanding the characteristic behavior of the cancer cells. But their analysis is limited to clustering and there is requirement to analyze what else can be done with the data for identifying the tumor subcategory and the stages of subclasses. In this work, we have extended the work with similar data (consisting of 105 breast tumor cell lines) to solve other detection and characterization problems through computation and intelligent representation learning. Most of our work comprises of systematic data cleaning, analysis, and building prediction models with deep computational architectures and establish that the transformed data can help in better distinction of the respective categories. Our main contribution is the novel gene-subcategory interaction-based regularization (GSIAR) based data selection and analysis concept, alongside the prediction, proven to enhance the performance of the classification techniques. Graphical Abstract A graphical abstract of our model - Gene-subcategory interaction affinity-based regularization (GSIAR).

Resonant X-ray enhancement of the Auger effect in high-Z atoms, molecules, and nanoparticles: potential biomedical applications.

It is shown that X-ray absorption can be considerably enhanced at resonant energies corresponding to K-shell excitation into higher shells with electron vacancies following Auger emissions in high-Z elements and compounds employed in biomedical applications. We calculate Auger resonant probabilities and cross sections to obtain total mass attenuation coefficients with resonant cross sections and detailed resonance structures corresponding to Kalpha, Kbeta, Kgamma, Kdelta, and Keta complexes lying between 6.4-7.1 keV in iron and 67-80 keV in gold. The basic parameters were computed using the relativistic atomic structure codes and the R-matrix codes. It is found that the average enhancement at resonant energies is up to a factor of 1000 or more for associated K --> L, M, N, O, P transitions. The resonant energies in high-Z elements such as gold are sufficiently high to ensure significant penetration in body tissue, and hence the possibility of achieving X-radiation dose reduction commensurate with resonant enhancements for cancer theranostics using high-Z nanoparticles and molecular radiosensitizing agents embedded in malignant tumors. The in situ deposition of X-ray energy, followed by secondary photon and electron emission, will be localized at the tumor site. We also note the relevance of this work to the development of novel monochromatic or narrow-band X-ray emission sources for medical diagnostics and therapeutics.

Electric quadrupole moments of the D states of alkaline-earth-metal ions.

The electric quadrupole moment for the 4d(2)D(5/2) state of (88)Sr(+); one of the most important candidates for an optical clock, has been calculated using the relativistic coupled-cluster theory. This is the first application of this theory to determine atomic electric quadrupole moments. The result of the calculation is presented and the important many-body contributions are highlighted. The calculated electric quadrupole moment is (2.94 +/- 0.07)ea(2)(0), where a(o) is the Bohr radius and the electronic charge while the measured value is (2.6 +/- 0.3) ea(2)(0). This is so far the most accurate determination of the electric quadrupole moment for the above mentioned state. We have also calculated the electric quadrupole moments for the metastable 4d(2)D(3/2) state of 88(Sr(+) and for the 3d(2)D(3/2.5/2) and 5d(2)D(3/2.5/2) states of (43)Ca(+) and (138)Ba(+), respectively.