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.

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.

Engagement of adolescents with ADHD in a narrative-centered game-based behavior change environment to reduce alcohol use.

Background: Attention deficit hyperactivity disorder (ADHD) affects about 13% of adolescents and is associated with substance use-related morbidity and mortality. While evidence on effective interventions to reduce alcohol use among adolescents with ADHD is limited, parent-teen communication about alcohol use has been found to be protective. Other approaches, such as educational interventions, hold promise to reduce alcohol-related harms in adolescents with ADHD.

"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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

An extension of Geiringer's theorem for a wide class of evolutionary search algorithms.

The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. The original Geiringer theorem tells us the limiting frequency of occurrence of a given individual under repeated application of crossover alone for the classical genetic algorithm. Recently, Geiringer's theorem has been generalized to include the case of linear GP with homologous crossover (which can also be thought of as a variable length GA).

State aggregation and population dynamics in linear systems.

We consider complex systems that are composed of many interacting elements, evolving under some dynamics. We are interested in characterizing the ways in which these elements may be grouped into higher-level, macroscopic states in a way that is compatible with those dynamics. Such groupings may then be thought of as naturally emergent properties of the system.

Evaluation of the current models for the evolution of bacterial DNA uptake signal sequences.

Current opinion considers two main hypotheses for the evolutionary origin of uptake signal sequences in bacteria: one model regards the uptake signal sequence (USS) as the result of biased gene conversion, whereas the second model views the USS as a molecular tag that evolved as an adaptation. In this article, we present various computational models that implement specific versions of those hypotheses. Those models show that the two hypothesis are not necessarily as opposed to each other as may appear at first glance.