A simple and natural interpretations of the DAMPE cosmic-ray electron/positron spectrum within two sigma deviations

The DArk Matter Particle Explorer (DAMPE) experiment has recently announced the first results for the measurement of total electron plus positron fluxes between 25 GeV and 4.6 TeV. A spectral break at about 0.9 TeV and a tentative peak excess around 1.4 TeV have been found. However, it is very difficult to reproduce both the peak signal and the smooth background including spectral break simultaneously. We point out that the numbers of events in the two energy ranges (bins) close to the 1.4 TeV excess have $1\sigma$ deficits. With the basic physics principles such as simplicity and naturalness, we consider the $-2\sigma$, $+2\sigma$, and $-1\sigma$ deviations due to statistical fluctuations for the 1229.3~GeV bin, 1411.4~GeV bin, and 1620.5~GeV bin. Interestingly, we show that all the DAMPE data can be explained consistently via both the continuous distributed pulsar and dark matter interpretations, which have $\chi^{2} \simeq 17.2$ and $\chi^{2} \simeq 13.9$ (for all the 38 points in DAMPE electron/positron spectrum with 3 of them revised), respectively. These results are different from the previous analyses by neglecting the 1.4 TeV excess. At the same time, we do a similar global fitting on the newly released CALET lepton data, which could also be interpreted by such configurations. Moreover, we present a $U(1)_D$ dark matter model with Breit-Wigner mechanism, which can provide the proper dark matter annihilation cross section and escape the CMB constraint. Furthermore, we suggest a few ways to test our proposal.Comment: 18 pages, 6 figures, 5 tables. Figures and Bibs update

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio

Understanding the in vivo Uptake Kinetics of a Phosphatidylethanolamine-binding Agent \u3csup\u3e99m\u3c/sup\u3eTc-Duramycin

Introduction 99mTc-Duramycin is a peptide-based molecular probe that binds specifically to phosphatidylethanolamine (PE). The goal was to characterize the kinetics of molecular interactions between 99mTc-Duramycin and the target tissue. Methods High level of accessible PE is induced in cardiac tissues by myocardial ischemia (30 min) and reperfusion (120 min) in Sprague–Dawley rats. Target binding and biodistribution of 99mTc-duramycin were captured using SPECT/CT. To quantify the binding kinetics, the presence of radioactivity in ischemic versus normal cardiac tissues was measured by gamma counting at 3, 10, 20, 60 and 180 min after injection. A partially inactivated form of 99mTc-Duramycin was analyzed in the same fashion. A compartment model was developed to quantify the uptake kinetics of 99mTc-Duramycin in normal and ischemic myocardial tissue. Results 99mTc-duramycin binds avidly to the damaged tissue with a high target-to-background radio. Compartment modeling shows that accessibility of binding sites in myocardial tissue to 99mTc-Duramycin is not a limiting factor and the rate constant of target binding in the target tissue is at 2.2 ml/nmol/min/g. The number of available binding sites for 99mTc-Duramycin in ischemic myocardium was estimated at 0.14 nmol/g. Covalent modification of D15 resulted in a 9-fold reduction in binding affinity. Conclusion 99mTc-Duramycin accumulates avidly in target tissues in a PE-dependent fashion. Model results reflect an efficient uptake mechanism, consistent with the low molecular weight of the radiopharmaceutical and the relatively high density of available binding sites. These data help better define the imaging utilities of 99mTc-Duramycin as a novel PE-binding agent