Integrated genomic and transcriptomic analyses of radiation-induced malignancies

Cancer is a genetic disease caused by an unregulated expansion of a clone of cells (Sompayrac, 2004). The genetic abnormalities in cancer are the consequences of defective DNA replication, repair, maintenance, and modification, genetic background, and exposure to mutagens (Alexandrov et al., 2013). Ionizing radiation (IR), a mutagen exposed to cancer patients during clinical radiotherapy (RT), can cause DNA damage, genomic instability, and mutagenesis (Sherborne et al., 2015). While RT has been effective in treating cancer, it increases the risk of second malignant neoplasm (SMN), a severe delayed complication associated with mainly pediatric cancer survivors many decades after the treatment of their first cancer (Robison & Hudson, 2014). As the mortality of patients with childhood cancer has been decreasing, cases of radiation-induced cancers has been increasing (Robison & Hudson, 2014). The considerable contribution by RT to SMN risk illustrate the need to characterize the genetic mechanism directly responsible for radiation-induced malignancies. To better our understanding of the mutational landscape of SMNs, our specific aims are to identify potential driver mutations implicated in radiation-induced malignancies through genome and transcriptome analysis and to assess whether genetic background, specifically germline polymorphisms and mutations in tumor suppressor gene TP53, has an impact on the formation of secondary malignancies

Hiddleston’s Causal Modeling Semantics and the Distinction between Forward-Tracking and Backtracking Counterfactuals

Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these two semantic treatments and argue that my (bifurcated) semantics is theoretically more promising than Hiddleston’s (unified) semantics

Neutrinos in the Simplest Little Higgs Model

The simplest little Higgs model based on a SU(3) global symmetry contains a $SU(3)_{weak}$ triplet and a singlet per a generation in the lepton sector. A neutral component of the triplet and the singlet turn into a neutral vector-like $SU(2)_L$ singlet after electroweak symmetry breaking while the other neutral component of the triplet is the SM neutrino. At tree level, Yukawa couplings of the lepton sector not only allow the neutral vector-like lepton to couple to the SM neutrino, but also give them a Dirac mass. Majorana mass terms for the SM neutrinos and their partners arise at one loops, leading to neutrino flavor mixing in addition to neutrino-heavy neutral lepton mixing.Comment: 13 pages, 3 figures, contents change

Knowledge and Pragmatic Factors

The stakes-shifting cases suggest that pragmatic factors such as stakes play an important role in determining our intuitive judgments of whether or not S knows that p. This seems to be in conflict with intellectualism, according to which pragmatic factors in general should not be taken into account, when considering whether or not S knows that p. This paper develops a theory of judgments of knowledge status that reconciles intellectualism with our intuitive judgments regarding the stakes-shifting cases. I argue that pragmatic factors affect only our epistemic perspectives, i.e., the ways in which we evaluate S’s epistemic position. Therefore, pragmatic factors only have an indirect impact on our judgments of knowledge status