Aberrant choroid plexus formation drives the development of treatment-related brain toxicity

Brain tumors are commonly treated with radiotherapy, but the efficacy of the treatment is limited by its toxicity to the normal tissue including post-irradiation contrast enhanced lesions often linked to necrosis. The poorly understood mechanisms behind such brain lesions were studied using cerebral organoids. Here we show that irradiation of such organoids leads to dose-dependent growth retardation and formation of liquid-filled cavities but is not correlated with necrosis. Instead, the radiation-induced changes comprise of an enhancement of cortical hem markers, altered neuroepithelial stem cell differentiation, and an increase of ZO1+/AQP1+/CLDN3+-choroid plexus (CP)-like structures accompanied by an upregulation of IGF2 mRNA, known to be expressed in CP and cerebrospinal fluid. The altered differentiation is attributed to changes in the WNT/BMP signaling pathways. We conclude that aberrant CP formation can be involved in radiation-induced brain lesions providing additional strategies for possible countermeasures

Injection optimization at particle accelerators via reinforcement learning: From simulation to real-world application

Optimizing the injection process in particle accelerators is crucial for enhancing beam quality and operational efficiency. This paper presents a framework for utilizing reinforcement learning (RL) to optimize the injection process at accelerator facilities. By framing the optimization challenge as an RL problem, we developed an agent capable of dynamically aligning the beam’s transverse space with desired targets. Our methodology leverages the soft actor-critic algorithm, enhanced with domain randomization and dense neural networks, to train the agent in simulated environments with varying dynamics promoting it to learn a generalized robust policy. The agent was evaluated in live runs at the cooler synchrotron COSY and it has successfully optimized the beam cross section reaching human operator level but in notably less time. An empirical study further validated the importance of each architecture component in achieving a robust and generalized optimization strategy. The results demonstrate the potential of RL in automating and improving optimization tasks at particle acceleration facilities