Abstract: Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. A user may request that a personalized agent join the user's gameplay session. The user device sends a request for the personalized agent to a game platform. The game platform determines whether the user has a license to execute a second instance of the game. When the user has a license to execute a second instance of the game, the second instance of the game may be executed on the user device. Information received from a personalized agent service is used to instantiate a personalized agent in the second instance of the game.

Abstract: The disclosed concepts relate to employing agent behavior models to control agent behavior in an application, such as a video game or a simulation. For instance, in some implementations, agent behavior models with relatively greater resource utilization, such as generative language models, are assigned to agents that are at higher levels of an agent hierarchy. Agent behavior models with relatively less resource utilization, such as reinforcement learning or hard-coded models, are assigned to agents that are at lower levels of the agent hierarchy.

Abstract: Example systems and methods for generating virtual immunofluorescence stains for tissue samples are provided. A computing device receives a slide image of a target tissue sample of a particular tissue type. The computing device selects a first trained machine learning (ML) model to generate virtual immunofluorescence (IF) stains of a first type for the particular tissue type based on a user input. The first trained ML model is trained at least based on a first set of stain images of a plurality of training tissue samples with stains of the first type. The computing device generate a virtually stained image of the target tissue sample with the virtual IF stains of the first type using the first trained ML model. The computing device displays the virtually stained image of the target tissue sample with the virtual IF stains of the first type.

Abstract: Described are systems and methods for evolving computer-implemented narrative games based on player feedback, levering generative artificial intelligence (AI), such as Generative Pre-Trained Transformer (GPT) or other foundation models, to create new game content responsive to player input, measure player engagement metrics during gameplay, and/or modify the game based on the generated new content and/or player engagement.

Abstract: Disclosed are methods and agents for predicting responses to therapy. More particularly, the present disclosure relates to methods and agents for detecting different forms of Programmed Death Ligand-1 (PD-L1) in cancer cells, which are useful for detecting location of PD-L1 in a cellular compartment (e.g., nucleus, cytoplasm, cell membrane) of a cancer cell, for predicting the likelihood of response of a cancer cell to therapy including immunotherapy, for stratifying a cancer patient as a likely responder or non-responder to a therapy, for managing treatment of a cancer patient, and for predicting clinical outcomes.

Abstract: Disclosed are compositions and methods suitable for treating coronavirus infections. More particularly, disclosed is a proteinaceous agent that prevents or inhibits the replication of a SARS-CoV virus, including a SARS-CoV-2 virus. Also disclosed is the use of these agents and molecules for treating or preventing a coronavirus infection (including a SARS-CoV-2 infection) in a subject.

Abstract: The present disclosure relates to systems and methods for using a director service as an intermediary management system to integrate interactive elements between a developer, a user, a generative machine learning (ML) model, and/or an interactive environment. In examples, the director service may receive input from a user or developer device relating to an interactive element from an interactive environment. The director service may process input from one or more of the developer, the user, and the interactive environment to recognize semantic context and intent objectives associated with the input. The director service may generate one or more prompts based on such input, which is processed by an ML model to generate output. In examples, the prompts may be provided to the ML model to direct it towards providing an output that is responsive to the input and one or more environment guidelines. The input and/or output may be multimodal.

Abstract: Disclosed are polypeptides which are covalently bound to molecular scaffolds such that two or more peptide loops are subtended between attachment points to the scaffold. More particularly, the invention provides peptide mimetics of PD-L1. Also disclosed are multimeric binding complexes of polypeptides which are covalently bound to molecular scaffolds such that two or more peptide loops are subtended between attachment points to the scaffold that are mimetics of PD-L1. Also disclosed are methods of using said peptides in treating a disease or disorder mediated by PD-L1 nuclear localisation.

Abstract: Aspects of the present disclosure provide systems and methods which utilizes machine learning techniques to provide enhanced accessibility features to a game. An accessibility service is provided which is capable of instantiating one or more machine learning models which can process current gameplay states and generate commands to assist users during gameplay. The accessibility commands may be provided to a game and used to supplement or modify user provided inputs in order to compensate for specific user needs. In further aspects, an accessibility user interface is provided which allows a user to dynamically enable or disable accessibility features during gameplay. The user interface is operable to receive accessibility selections and provide the selection data to an accessibility service during gameplay.

Abstract: Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. A user may request that a personalized agent join the user's gameplay session. The user device sends a request for the personalized agent to a game platform. The game platform determines whether the user has a license to execute a second instance of the game. When the user has a license to execute a second instance of the game, the second instance of the game may be executed on the user device. Information received from a personalized agent service is used to instantiate a personalized agent in the second instance of the game.

Abstract: Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. As the user continues to play with the agent, the one or more machine learning models develop gameplay styles for the agent that complement the user's preferred playstyle, incorporate the user's preferred strategies, and is generally customized for interaction with the user. The agent personalization data generated during gameplay is stored by the service. An application programming interface is provided by the personalized agent service. Using the API, games can import agent personalization data in order to customize in-game non-player characters (NPCs), thereby customizing the in-game NPCs in accordance with the user's preferences.

Abstract: In examples, a developer may define a set of computer-controlled agent attributes, which may be processed by a generative multimodal machine learning model in conjunction with background information associated with a virtual environment (e.g., “lore”) and other agent information to generate multimodal model output with which to control the behavior of the computer-controlled agent. Thus, a player may interact with the computer-controlled agent, such that user input from the player is processed using the ML model to generate model output to affect the behavior of the computer-controlled agent, thereby enabling the user and the computer-controlled agent to interact. As compared to manual dialogue authoring, use of agent information to define the behavior of a computer-controlled agent may result in reduced effort on the part of a creator while also offering increased depth and variety for computer-controlled agents of a virtual environment.

Abstract: Aspects of the present disclosure relate to grounded multimodal agent interactions, where a user input is processed using a multimodal machine learning model to generate model output. The model output may then be processed to affect the behavior of an application, for example to enable a user to control the application and/or to facilitate user interactions with a conversational agent, among other examples. In some instances, at least a part of the model output may be executed or parsed, for example to call an application programming interface or function of the application. Thus, use of a multimodal machine learning model according to aspects described herein may enable the use of user-provided natural language input to affect the behavior of an application accordingly.

Abstract: Aspects of the present disclosure relate to a machine learning-based dialogue authoring environment. In examples, a developer or creator of a virtual environment may use a generative multimodal machine learning (ML) model to create or otherwise update aspects of a dialogue tree for one or more computer-controlled agents and/or players of the virtual environment. For example, the developer may provide an indication of context associated with the dialogue for use by the ML model, such that the ML model may generate a set of candidate interactions accordingly. The developer may select a subset of the candidate interactions for inclusion in the dialogue tree, which may then be used to generate associated nodes within the tree accordingly. Thus, nodes in the dialogue tree may be iteratively defined based on model output of the ML model, thereby assisting the developer with dialogue authoring for the virtual environment.

Abstract: Aspects of the present disclosure relate to grounded multimodal agent interactions, where a user input is processed using a multimodal machine learning model to generate model output. The model output may then be processed to affect the behavior of an application, for example to enable a user to control the application and/or to facilitate user interactions with a conversational agent, among other examples. In some instances, at least a part of the model output may be executed or parsed, for example to call an application programming interface or function of the application. Thus, use of a multimodal machine learning model according to aspects described herein may enable the use of user-provided natural language input to affect the behavior of an application accordingly.

Abstract: This disclosure relates generally to methods and agents for assessing T-cell function and for predicting responses to therapy. More particularly, the present invention relates to methods and agents for detecting different forms of Eomesodermin (EOMES) in T-cells, which are useful for assessing the function of a T-cell, for assessing the immune function of a subject, for predicting the likelihood of response of a cancer patient to therapy including immunotherapy, for stratifying a cancer patient as a likely responder or non-responder to a therapy, and for managing treatment of a cancer patient.

Abstract: Disclosed are lysine specific histone demethylase-1 (LSD1) inhibitors in methods and compositions for immune check-point inhibition. The invention also relates to proteinaceous molecules and their use in altering at least one of (i) formation, (ii) 5 N proliferation, (iii) maintenance, (iv) epithelial to mesenchymal cell transition (EMT), or (v) mesenchymal to epithelial cell transition (MET) of an LSD1 overexpressing cell.

Abstract: The present invention relates to a composition for enhancing T-cell function or for treating a T-cell dysfunctional disorder, the composition comprising, consisting or consisting essentially of a lysine specific demethylase (LSD) inhibitor (which may be a MAO inhibitor or phenelzine) and a Programmed cell death protein-1 (PD-1) binding antagonist (which may be an antibody, preferably nivolumab, pembrolizumab, lambrolizumab or pidilizumab).

Abstract: Disclosed are compositions and methods that use lysine demethylase inhibitors for inhibiting the growth of cancer stem cells or tumor initiating cells, for enhancing the biological effects of chemotherapeutic drugs or irradiation on cancer cells and/or for preventing cancer recurrence.

Abstract: Disclosed are methods and agents for predicting responses to therapy. More particularly, the present disclosure relates to methods and agents for detecting different forms of Programmed Death Ligand-1 (PD-L1) in cancer cells, which are useful for detecting location of PD-L1 in a cellular compartment (e.g., nucleus, cytoplasm, cell membrane) of a cancer cell, for predicting the likelihood of response of a cancer cell to therapy including immunotherapy, for stratifying a cancer patient as a likely responder or non-responder to a therapy, for managing treatment of a cancer patient, and for predicting clinical outcomes.