Towards improved psychological outcomes for survivors of burn injuries

Editoria

The Dynamics of Viral Marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective

DOK2 inhibits EGFR-mutated lung adenocarcinoma

Somatic mutations in the EGFR proto-oncogene occur in ~15% of human lung adenocarcinomas and the importance of EGFR mutations for the initiation and maintenance of lung cancer is well established from mouse models and cancer therapy trials in human lung cancer patients. Recently, we identified DOK2 as a lung adenocarcinoma tumor suppressor gene. Here we show that genomic loss of DOK2 is associated with EGFR mutations in human lung adenocarcinoma, and we hypothesized that loss of DOK2 might therefore cooperate with EGFR mutations to promote lung tumorigenesis. We tested this hypothesis using genetically engineered mouse models and find that loss of Dok2 in the mouse accelerates lung tumorigenesis initiated by oncogenic EGFR, but not that initiated by mutated Kras. Moreover, we find that DOK2 participates in a negative feedback loop that opposes mutated EGFR; EGFR mutation leads to recruitment of DOK2 to EGFR and DOK2-mediated inhibition of downstream activation of RAS. These data identify DOK2 as a tumor suppressor in EGFR-mutant lung adenocarcinoma

Giant-Resonances in Ca-40

Journals published by the American Physical Society can be found at http://publish.aps.org

Inferring hidden Markov models from noisy time sequences: a method to alleviate degeneracy in molecular dynamics

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al., and generates so called causal state models, equivalent to hidden Markov models. This method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods.Comment: 19 pages, 9 figure