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The emergence of photorealistic and cinematic non-player character (NPC) animation presents new challenges for video game developers. Game player expectations of cinematic acting styles bring a more sophisticated aesthetic in the representation of social interaction. New methods can streamline workflow by integrating actor-driven character design into the development of game character AI and animation. A workflow that tracks actor performance to final neural network (NN) design depends on a rigorous method of producing single-actor video corpora from which to train emotion AI NN models. While numerous video corpora have been developed to study emotion elicitation of the face from which to test theoretical models and train neural networks to recognize emotion, developing single-actor corpora to train NNs of NPCs in video games is uncommon. A class of facial emotion recognition (FER) products have enabled production of single-actor video corpora that use emotion analysis data. This paper introduces a single-actor game character corpora workflow for game character developers. The proposed method uses a single actor video corpus and dataset with the intent to train and implement a NN in an off-the-shelf video game engine for facial animation of an NPC. The efficacy of using a NN-driven animation controller has already been demonstrated (Schiffer, 2021, Kozasa et. al 2006). This paper focuses on using a single-actor video corpus for the purpose of training a NN-driven animation controller. 

Cell-free protein synthesis (CFPS) is a platform technology that provides new opportunities for protein expression, metabolic engineering, therapeutic development, education, and more. The advantages of CFPS over in vivo protein expression include its open system, the elimination of reliance on living cells, and the ability to focus all system energy on production of the protein of interest. Over the last 60 years, the CFPS platform has grown and diversified greatly, and it continues to evolve today. Both new applications and new types of extracts based on a variety of organisms are current areas of development. However, new users interested in CFPS may find it challenging to implement a cell-free platform in their laboratory due to the technical and functional considerations involved in choosing and executing a platform that best suits their needs. Here we hope to reduce this barrier to implementing CFPS by clarifying the similarities and differences amongst cell-free platforms, highlighting the various applications that have been accomplished in each of them, and detailing the main methodological and instrumental requirement for their preparation. Additionally, this review will help to contextualize the landscape of work that has been done using CFPS and showcase the diversity of applications that it enables.