[Targeting the spliceosome: A new therapeutic strategy to counteract chemotherapy resistance in lung cancer?].

Lung cancer is the first cancer-related cause of death worldwide. This is in partially due to therapeutic resistance, which occurs in around 70% of patients, especially those receiving platinum salts, the gold-standard chemotherapy. The massive deregulation of alternative transcript splicing processes observed in many cancers has led to the development of a new class of pharmacological agents aimed at inhibiting the activity of the splicing machinery (spliceosome).

Interacting branching process as a simple model of innovation.

We describe innovation in terms of a generalized branching process. Each new invention pairs with any existing one to produce a number of offspring, which is Poisson distributed with mean p. Existing inventions die with probability p/τ at each generation.

Attractor and basin entropies of random Boolean networks under asynchronous stochastic update.

We introduce a method to study random Boolean networks with asynchronous stochastic update. Each node in the state space network starts with equal occupation probability, which then evolves to a steady state. Attractors and the sizes of their basins are determined by the nodes left occupied at late times.

Self-organization in two-dimensional swarms.

We undertake a systematic numerical exploration of self-organized states in a deterministic model of interacting self-propelled particles in two dimensions. In the process, we identify various types of collective motion, namely, disordered swarms, rings, and droplets. We construct a "phase diagram," which summarizes our results as it delineates phase transitions (all discontinuous) between disordered swarms and vortical flocks on one hand and bound vortical flocks and expanding formations on the other.

Activity-dependent branching ratios in stocks, solar x-ray flux, and the Bak-Tang-Wiesenfeld sandpile model.

We define an activity-dependent branching ratio that allows comparison of different time series X(t). The branching ratio b(x) is defined as b(x)=E[xi(x)/x]. The random variable xi(x) is the value of the next signal given that the previous one is equal to x, so xi(x)=[X(t+1) | X(t)=x].