Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance.

A mammalian cell houses two genomes located separately in the nucleus and mitochondria. During evolution, communications and adaptations between these two genomes occur extensively to achieve and sustain homeostasis for cellular functions and regeneration. Mitochondria provide the major cellular energy and contribute to gene regulation in the nucleus, whereas more than 98% of mitochondrial proteins are encoded by the nuclear genome. Such two-way signaling traffic presents an orchestrated dynamic between energy metabolism and consumption in cells. Recent reports have elucidated the way how mitochondrial bioenergetics synchronizes with the energy consumption for cell cycle progression mediated by cyclin B1/CDK1 as the communicator. This review is to recapitulate cyclin B1/CDK1 mediated mitochondrial activities in cell cycle progression and stress response as well as its potential link to reprogram energy metabolism in tumor adaptive resistance. Cyclin B1/CDK1-mediated mitochondrial bioenergetics is applied as an example to show how mitochondria could timely sense the cellular fuel demand and then coordinate ATP output. Such nucleus-mitochondria oscillation may play key roles in the flexible bioenergetics required for tumor cell survival and compromising the efficacy of anti-cancer therapy. Further deciphering the cyclin B1/CDK1-controlled mitochondrial metabolism may invent effect targets to treat resistant cancers

Video Game AI Algorithms

The ubiquity of human-like characters in video games presents the challenge of implementing human-like behaviors. To address the pathfinding and behavior selection problems faced in a real project, we came up with two improved methods based upon mainstream solutions. To make pathfinding agent take into account more incentives than only a destination, We designed a new pathfinding algorithm named Cost Radiation A* (CRA*), based on the A* heuristic search algorithm. CRA* incorporates the agent\u27s preference for other objects, represented as cost radiators in our scheme. We also want to enable non-player characters (NPCs) to learn in real-time in response to a player\u27s actions. We adopt the behavior tree framework, and design a new composite node for it, named learner node, which enables developers to design learning behaviors. The learner node achieves basic reinforcement learning but is also open to more sophisticated use