Fragmentation and Multifragmentation of 10.6A GeV Gold Nuclei

We present the results of a study performed on the interactions of 10.6A GeV gold nuclei in nuclear emulsions. In a minimum bias sample of 1311 interac- tions, 5260 helium nuclei and 2622 heavy fragments were observed as Au projec- tile fragments. The experimental data are analyzed with particular emphasis of target separation interactions in emulsions and study of criticalexponents. Multiplicity distributions of the fast-moving projectile fragments are inves- tigated. Charged fragment moments, conditional moments as well as two and three -body asymmetries of the fast moving projectile particles are determined in terms of the total charge remaining bound in the multiply charged projectile fragments. Some differences in the average yields of helium nuclei and heavier fragments are observed, which may be attributed to a target effect. However, two and three-body asymmetries and conditional moments indicate that the breakup mechanism of the projectile seems to be independent of target mass. We looked for evidence of critical point observable in finite nuclei by study the resulting charged fragments distributions. We have obtained the values for the critical exponents gamma, beta and tau and compare our results with those at lower energy experiment (1.0A GeV data). The values suggest that a phase transition like behavior, is observed.Comment: latex, revtex, 28 pages, 12 figures, 3tables, submitted to Europysics Journal

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer.

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article