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Understanding and optimizing adolescent-specific engagement with behavior change interventions will open doors for providers to promote healthy changes in an age group that is simultaneously difficult to engage and especially important to affect. For digital interventions, there is untapped potential in combining the vastness of process-level data with the analytical power of artificial intelligence (AI) to understand not only how adolescents engage but also how to improve upon interventions with the goal of increasing engagement and, ultimately, efficacy. Rooted in the example of the INSPIRE narrative-centered digital health behavior change intervention (DHBCI) for adolescent risky behaviors around alcohol use, we propose a framework for harnessing AI to accomplish 4 goals that are pertinent to health care providers and software developers alike: measurement of adolescent engagement, modeling of adolescent engagement, optimization of current interventions, and generation of novel interventions. Operationalization of this framework with youths must be situated in the ethical use of this technology, and we have outlined the potential pitfalls of AI with particular attention to privacy concerns for adolescents. Given how recently AI advances have opened up these possibilities in this field, the opportunities for further investigation are plenty., (©Alison Giovanelli, Jonathan Rowe, Madelynn Taylor, Mark Berna, Kathleen P Tebb, Carlos Penilla, Marianne Pugatch, James Lester, Elizabeth M Ozer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.05.2023.)

Interactive narrative technologies for preventive health care offer significant potential for promoting health behavior change in adolescents. By improving adolescents' knowledge, personal efficacy, and self-regulatory skills these technologies hold great promise for realizing positive impacts on adolescent health. These potential benefits are enabled through story-centric learning experiences that provide opportunities for adolescents to practice strategies to reduce risky health behaviors in engaging game-based environments. A distinctive feature of interactive narrative that promotes engagement is players' ability to influence the story through the choices they make. In this paper, we present initial work investigating engagement in an interactive narrative that focuses on reducing adolescents' risky behaviors around alcohol use. Specifically, we consider how the short-term and long-term goals adolescents choose as being important to the protagonist character relates to their engagement with the interactive narrative. Leveraging interaction log data from a pilot study with 20 adolescents, we conduct a cluster-based analysis of the goals players selected. We then examine how engagement differs between the identified clusters. Results indicate that adolescents' choices for the protagonist's short-term and long-term goals can significantly impact their engagement with the interactive narrative.

Photonics based pre-clinical imaging is an extensively used technique to allow for the study of biologically relevant activity typically within a small-mouse model. Namely, bioluminescent tomography (BLT) attempts to tomographically reconstruct the 3-dimensional spatial light distribution of luminophores within a small animal given surface light measurements and known underlying optical parameters. Often it is the case where these optical parameters are unknown leading to the use of a 'best' guess approach or to direct measurements using either a multi-modal or dedicated system. Using these conventional approaches can lead to both inaccurate results and extending periods of imaging time. This work introduces the development of an algorithm that is used to accurately localize the spatial light distribution from a bioluminescence source within a subject by simultaneously reconstructing both the underlying optical properties and source spatial distribution and intensity from the same set of surface measurements. Through its application in 2- and 3-dimensional, homogeneous and heterogenous numerical models, it is demonstrated that the proposed algorithm is capable of replicating results as compared to 'gold' standard where the absolute optical properties are known. Additionally, the algorithm has been applied to experimental data using a tissue mimicking block phantom, recovering a spatial light distribution that has a localization error of ∼1.53 mm, which is better than previously published results without the need of assumptions regarding the underlying optical properties or source distribution., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

Purpose: Accidents and unintentional injuries account for the greatest number of adolescent deaths, often involving use of alcohol and other substances. This article describes the iterative design and development of Interactive Narrative System for Patient-Individualized Reflective Exploration (INSPIRE), a narrative-centered behavior change environment for adolescents focused on reducing alcohol use. INSPIRE is designed to serve as an extension to clinical preventive care, engaging adolescents in a theoretically grounded intervention for health behavior change by leveraging 3D game engine and interactive narrative technologies., Methods: Adolescents were engaged in all aspects of the iterative, multiyear development process of INSPIRE through over 20 focus groups and iterative pilot testing involving more than 145 adolescents. Qualitative findings from focus groups are reported, as well as quantitative findings from small-scale pilot sessions investigating adolescent engagement with a prototype version of INSPIRE using a combination of questionnaire and interaction trace log data., Results: Adolescents reported that they found INSPIRE to be engaging, believable, and relevant to their lives. The majority of participants indicated that the narrative's protagonist character was like them (84%) and that the narrative featured virtual characters that they could relate to (79%). In the interactive narrative, the goals most frequently chosen by adolescents were "stay in control" (60%) and "do not get in trouble" (55%)., Conclusions: With a strong theoretical framework (social-cognitive behavior change theory) and technology advances (narrative-centered learning environments), the field is well positioned to design health behavior change systems that can realize significant impacts on behavior change for adolescent preventive health., (Copyright © 2020 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

Recent advances in artificial intelligence (AI) are creating new opportunities for personalizing technology-based health interventions to adolescents. This article provides a computer science perspective on how emerging AI technologies-intelligent learning environments, interactive narrative generation, user modeling, and adaptive coaching-can be utilized to model adolescent learning and engagement and deliver personalized support in adaptive health technologies. Many of these technologies have emerged from human-centered applications of AI in education, training, and entertainment. However, their application to improving healthcare, to date, has been comparatively limited. We illustrate the opportunities provided by AI-driven adaptive technologies for adolescent preventive healthcare by describing a vision of how future adolescent preventive health interventions might be delivered both inside and outside of the clinic. Key challenges posed by AI-driven health technologies are also presented, including issues of privacy, ethics, encoded bias, and integration into clinical workflows and adolescent lives. Examples of empirical findings about the effectiveness of AI technologies for user modeling and adaptive coaching are presented, which underscore their promise for application toward adolescent health. The article concludes with a brief discussion of future research directions for the field, which is well positioned to leverage AI to improve adolescent health and well-being., (Copyright © 2020. Published by Elsevier Inc.)

Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ∼3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

The complexity of biological models makes methods for their analysis and understanding highly desirable. Here, we demonstrate the orchestration of various novel coarse-graining methods by applying them to the mitotic spindle assembly checkpoint. We begin with a detailed fine-grained spatial model in which individual molecules are simulated moving and reacting in a three-dimensional space. A sequence of manual and automatic coarse-grainings finally leads to the coarsest deterministic and stochastic models containing only four molecular species and four states for each kinetochore, respectively. We are able to relate each more coarse-grained level to a finer one, which allows us to relate model parameters between coarse-grainings and which provides a more precise meaning for the elements of the more abstract models. Furthermore, we discuss how organizational coarse-graining can be applied to spatial dynamics by showing spatial organizations during mitotic checkpoint inactivation. We demonstrate how these models lead to insights if the model has different "meaningful" behaviors that differ in the set of (molecular) species. We conclude that understanding, modeling and analyzing complex bio-molecular systems can greatly benefit from a set of coarse-graining methods that, ideally, can be automatically applied and that allow the different levels of abstraction to be related.

This article presents an exploratory landscape analysis of three NP-hard combinatorial optimisation problems: the number partitioning problem, the binary knapsack problem, and the quadratic binary knapsack problem. In the article, we examine empirically a number of fitness landscape properties of randomly generated instances of these problems. We believe that the studied properties give insight into the structure of the problem landscape and can be representative of the problem difficulty, in particular with respect to local search algorithms. Our work focuses on studying how these properties vary with different values of problem parameters. We also compare these properties across various landscapes that were induced by different penalty functions and different neighbourhood operators. Unlike existing studies of these problems, we study instances generated at random from various distributions. We found a general trend where some of the landscape features in all of the three problems were found to vary between the different distributions. We captured this variation by a single, easy to calculate parameter and we showed that it has a potentially useful application in guiding the choice of the neighbourhood operator of some local search heuristics.

Effective quality risk management is fundamental to ensuring the protection of human subjects and reliability of clinical trial results during the conduct of clinical trials. Quality risk management supports effective delivery of clinical development programs and ultimately delivery of treatments to patients. Thus, risk management is a core element of an effective quality management system (QMS) as described in the TransCelerate Clinical Quality Management System (CQMS) conceptual framework. In addition, the landscape of quality risk management in clinical development evolves as regulatory authorities adopt elements of risk management to promote proactive quality management. This paper's goal is to provide a conceptual framework for quality risk management as part of a CQMS. The components of a quality risk management program are explored including foundational elements and quality risk management methods appropriate for clinical development.

Chemical organisation theory is a framework developed to simplify the analysis of long-term behaviour of chemical systems. In this work, we build on these ideas to develop novel techniques for formal quantitative analysis of chemical reaction networks, using discrete stochastic models represented as continuous-time Markov chains. We propose methods to identify organisations, and to study quantitative properties regarding movements between these organisations. We then construct and formalise a coarse-grained Markov chain model of hierarchic organisations for a given reaction network, which can be used to approximate the behaviour of the original reaction network. As an application of the coarse-grained model, we predict the behaviour of the reaction network systems over time via the master equation. Experiments show that our predictions can mimic the main pattern of the concrete behaviour in the long run, but the precision varies for different models and reaction rule rates. Finally, we propose an algorithm to selectively refine the coarse-grained models and show experiments demonstrating that the precision of the prediction has been improved.