No abstract
Tendon injuries are common and often treated surgically, however, current tendon repair healing results in poorly organized fibrotic tissue. While certain growth factors have been reported to improve both the strength and organization of the repaired enthesis, their clinical applicability is severely limited due to a lack of appropriate delivery strategies. In this study, we evaluated a recently developed fluorescent probe, Osteoadsorptive Fluorogenic Sentinel‐3 that is composed of a bone‐targeting bisphosphonate (BP) moiety linked to fluorochrome and quencher molecules joined via a cathepsin K‐sensitive peptide sequence. Using a murine Achilles tendon‐to‐bone repair model, BP‐based and/or Ctsk‐coupled imaging probes were applied either locally or systemically. Fluorescence imaging was used to quantify the resultant signal in vivo. After tendon‐bone repair, animals that received either local or systemic administration of imaging probes demonstrated significantly higher fluorescence signal at the repair site compared to the sham surgery group at all time points (p < 0.001), with signal peaking at 7–10 days after surgery. Our findings demonstrate the feasibility of using a novel BP‐based targeting and Ctsk‐activated delivery of molecules to the site of tendon‐to‐bone repair and creates a foundation for further development of this platform as an effective strategy to deliver bioactive molecules to sites of musculoskeletal injury.
Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights.
Platformer games are popular in the video game industry and their design require much efforts from game companies. As part of the design process, playtesting is key for improving the gameplay before game release. Playtesting is the quality assurance phase of the game development cycle where people are hired to play the game, report bugs and provide feedback regarding the playability of the game. This feedback could be used for game balancing (process of tuning game rules to prevent them from being ineffective or provide undesirable results). However, playtesting may be expensive if done manually and may require several iterations, resulting in high budget requirement and time for game companies. In this thesis, we investigate a way to automatically playtest 2D platformer levels using a combination of deep reinforcement learning and curriculum learning, for both quality assurance and game balancing. Deep Reinforcement Learning has contributed greatly in playing games (Atari and Dota 2) and in this thesis, we will try to replicate the results to playtest games. Curriculum learning is an approach that has shown promising results thus we will explore it to derive useful results. We develop our APT tool by training an artificial intelligence (AI) agent on several different platformer levels following a curriculum, and use the trained agent to playtest newly-created levels. Our APT is able to identify areas of the level that needed design improvements and further gameplay balancing. We contribute a reliable APT tool for designers that wish to easily design 2D platformer games and a discussion of how our results extend to APT at-large.
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