We review common extensions of particle-in-cell (PIC) schemes which account for strong field phenomena in laser-plasma interactions. After describing the physical processes of interest and their numerical implementation, we provide solutions for several associated methodological and algorithmic problems. We propose a modified event generator that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step. Based on this, we also develop an adaptive event generator that subdivides the time step for locally resolving QED events, allowing for efficient simulation of cascades. Further, we present a unified technical interface for including the processes of interest in different PIC implementations. Two PIC codes which support this interface, picador and elmis, are also briefly reviewed.

Electromagnetic cascades have the potential to act as a high-energy photon source of unprecedented brightness. Such a source would offer new experimental possibilities in fundamental science, but in the cascade process radiation reaction and rapid electron-positron plasma production seemingly restrict the efficient production of photons to sub-GeV energies. Here, we show how to overcome these energetic restrictions and how to create a directed GeV photon source, with unique capabilities as compared to existing sources. Our new source concept is based on a controlled interplay between the cascade and anomalous radiative trapping. Using specially designed advanced numerical models supported with analytical estimates, we demonstrate that the concept becomes feasible at laser powers of around 7 PW, which is accessible at soon-to-be-available facilities. A higher peak power of 40 PW can provide $10^9$ photons with GeV energies in a well-collimated 3-fs beam, achieving peak brilliance $9\times {10}^{24}\mathrm{ph}{\mathrm{s}}^{-1}{\mathrm{mrad}}^{-2}{\mathrm{mm}}^{-2}/0.1\%\mathrm{BW}$.

We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93−99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.