Narrative Play-Promoting Adolescent Health With Interactive Games.

This Arts and Medicine feature discusses INSPIRE, a digital health game designed to foster adolescent health behavior change.

Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions.

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.

Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation Problems.

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that UMDA and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SubsetSum, Knapsack, and SetCover. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multiobjective problems (CountingOnesCountingZeros and a multiobjective formulation of SetCover). We compare several adaptations of UMDA for multiobjective problems with the Simple Evolutionary Multiobjective Optimiser (SEMO) and NSGA-II. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms.

Quantitative molecular bioluminescence tomography.

SIGNIFICANCE: Bioluminescence imaging and tomography (BLT) are used to study biologically relevant activity, typically within a mouse model. A major limitation is that the underlying optical properties of the volume are unknown, leading to the use of a "best" estimate approach often compromising quantitative accuracy. AIM: An optimization algorithm is presented that localizes the spatial distribution of bioluminescence by simultaneously recovering the optical properties and location of bioluminescence source from the same set of surface measurements. APPROACH: Measured data, using implanted self-illuminating sources as well as an orthotopic glioblastoma mouse model, are employed to recover three-dimensional spatial distribution of the bioluminescence source using a multi-parameter optimization algorithm. RESULTS: The proposed algorithm is able to recover the size and location of the bioluminescence source while accounting for tissue attenuation. Localization accuracies of <1 mm are obtained in all cases, which is similar if not better than current "gold standard" methods that predict optical properties using a different imaging modality. CONCLUSIONS: Application of this approach, using in-vivo experimental data has shown that quantitative BLT is possible without the need for any prior knowledge about optical parameters, paving the way toward quantitative molecular imaging of exogenous and indigenous biological tumor functionality.

"What's Important to You, Max?": The Influence of Goals on Engagement in an Interactive Narrative for Adolescent Health Behavior Change.

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.

Simultaneous diffuse optical and bioluminescence tomography to account for signal attenuation to improve source localization.

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.

Fostering Engagement in Health Behavior Change: Iterative Development of an Interactive Narrative Environment to Enhance Adolescent Preventive Health Services.

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.

Artificial Intelligence for Personalized Preventive Adolescent Healthcare.

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.

Single pixel hyperspectral bioluminescence tomography based on compressive sensing.

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.

Multi-scale stochastic organization-oriented coarse-graining exemplified on the human mitotic checkpoint.

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.

Quality Risk Management Framework: Guidance for Successful Implementation of Risk Management in Clinical Development.

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.

Landscape Analysis of a Class of NP-Hard Binary Packing Problems.

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.

Organisation-Oriented Coarse Graining and Refinement of Stochastic Reaction Networks.

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.

Effects of eicosapentaenoic acid and docosahexaenoic acid on low-density lipoprotein cholesterol and other lipids: a review.

In this exploratory, hypothesis-generating literature review, we evaluated potentially differential effects of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and non-HDL-C in published studies of ω-3 fatty acid supplementation or prescription ω-3 fatty acid ethyl esters. Placebo-adjusted changes in mean lipid parameters were compared in randomized, controlled trials in subjects treated for ≥ 4 weeks with DHA or EPA. Of 22 studies identified, 6 compared DHA with EPA directly, 12 studied DHA alone (including 14 DHA-treated groups), and 4 examined EPA alone. In studies directly comparing EPA with DHA, a net increase in LDL-C of 3.3% was observed with DHA (DHA: +2.6%; EPA: -0.7%). In such head-to-head comparative studies, DHA treatment was associated with a net decrease in TG by 6.8% (DHA: -22.4%; EPA: -15.6%); a net increase in non-HDL-C by 1.7% (DHA: -1.2%; EPA -2.9%); and a net increase in HDL-C by 5.9% (DHA: +7.3%; EPA: +1.4%). Increases in LDL-C were also observed in 71% of DHA-alone groups [with demonstrated statistical significance (P < .05) in 67% (8 of 12) DHA-alone studies] but not in any EPA-alone studies. Changes in LDL-C significantly correlated with baseline TG for DHA-treated groups. The range of HDL-C increases documented in DHA-alone vs EPA-alone studies further supports the fact that HDL-C is increased more substantially by DHA than EPA. In total, these findings suggest that DHA-containing supplements or therapies were associated with more significant increases in LDL-C and HDL-C than were EPA-containing supplements or therapies. Future prospective, randomized trials are warranted to confirm these preliminary findings, determine the potential effects of these fatty acids on other clinical outcomes, and evaluate the generalizability of the data to larger and more heterogeneous patient populations.

Representation invariant genetic operators.

A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action. We then consider the problem of characterizing crossover and mutation operators that have such invariance properties. In the case where the corresponding group action acts transitively on the search space, we provide a complete characterization, including high-level representation-independent algorithms implementing these operators.

Molecular circuits for associative learning in single-celled organisms.

We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.

Storage, preparation, and usage of fortified food aid among Guatemalan, Ugandan, and Malawian beneficiaries: a field study report.

BACKGROUND: An important consideration in determining the ability of fortified food-aid commodities to meet the nutritional needs of beneficiaries is the manner in which commodities are utilized and prepared and the degree to which micronutrient losses occur during handling and cooking by the beneficiaries. OBJECTIVE: A field study was conducted in Uganda, Malawi, and Guatemala to obtain data on storage, preparation, and usage of fortified blended foods provided by the US Agency for International Development. METHODS: Interview and observational data on the use of corn-soy blend, cornmeal, soy-fortified cornmeal, soy-fortified bulgur, and fortified vegetable oil were collected from more than 100 households and two wet-feeding sites (where food is prepared and served by staff on-site) in 32 villages. RESULTS: Storage practices by beneficiaries appeared to be appropriate, and all commodities observed were free from off-flavors and odors. Cooking water was typically obtained from boreholes or open wells with a pH range of 4.7 to 7.7 Food preparation usually took place in covered areas with the use of an aluminum or clay pot over a wood-fueled fire. Thin or thick porridges were the most common dishes prepared from cereal-based products, with concentration ranges of 10% to 31% (wt/ wt) in water. Cooking times for porridges ranged from 5 to 53 minutes, with a mean of 26 minutes. Tortillas and beverages were other preparations commonly observed in Guatemala. Vegetable oil was typically used for pan frying. CONCLUSIONS: Cooking fuel could be saved and nutritional quality probably improved if relief agencies emphasized shorter cooking times. These data can be used to simulate preparation methods in the laboratory for assessment of the nutritional impact of cooking.

The origin of autonomous agents by natural selection.

We propose conditions in which an autonomous agent could arise, and increase in complexity. It is assumed that on the primitive Earth there arose a recycling flow-reactor containing spontaneously formed oil droplets or lipid aggregates. These droplets grew at a basal rate by simple incorporation of lipid phase material, and divided by external agitation. This type of system was able to implement a natural selection algorithm once heredity was added. Macroevolution became possible by selection for rarely occurring chemical reactions that produced holistic autocatalytic molecular replicators (contained within the aggregate) capable of doubling at least as fast as the lipid aggregate, and which were also capable of benefiting the growth of its lipid aggregate container. No nucleotides or monomers capable of modular heredity were required at the outset. To explicitly state this hypothesis, a computer model was developed that employed an artificial chemistry, exhibiting conservation of mass and energy, incorporated within each individual of a population of lipid aggregates. This model evolved increasingly complex self-sustaining processes of constitution, a result that is also expected in real chemistry.

Natural selection in chemical evolution.

We propose that chemical evolution can take place by natural selection if a geophysical process is capable of heterotrophic formation of liposomes that grow at some base rate, divide by external agitation, and are subject to stochastic chemical avalanches, in the absence of nucleotides or any monomers capable of modular heredity. We model this process using a simple hill-climbing algorithm, and an artificial chemistry that is unique in exhibiting conservation of mass and energy in an open thermodynamic system. Selection at the liposome level results in the stabilization of rarely occurring molecular autocatalysts that either catalyse or are consumed in reactions that confer liposome level fitness; typically they contribute in parallel to an increasingly conserved intermediary metabolism. Loss of competing autocatalysts can sometimes be adaptive. Steady-state energy flux by the individual increases due to the energetic demands of growth, but also of memory, i.e. maintaining variations in the chemical network. Self-organizing principles such as those proposed by Kauffman, Fontana, and Morowitz have been hypothesized as an ordering principle in chemical evolution, rather than chemical evolution by natural selection. We reject those notions as either logically flawed or at best insufficient in the absence of natural selection. Finally, a finite population model without elitism shows the practical evolutionary constraints for achieving chemical evolution by natural selection in the lab.

Markov models of integrating spheres for hyperspectral imaging.

Theoretical models of the signal detected by a CCD camera during hyperspectral imaging with an integrating sphere are derived using Markov chains with absorbing states. The models provide analytical expressions that describe the real reflectance of the sample as a function of the detected signal at each pixel of the image. Validation of the models was done by using reflectance standards and tissue phantoms. The models provide accurate analytical solutions for samples and spheres that are near-Lambertian reflectors.