Family health informatics tools can help support well-being with shared data tracking. Prior work typically focused on shared data review, but often in specific moments, like bedtime, or centered on caregiving of children or elderly members. To investigate how tracking can support mutual health collaboration between family members pervasively across daily contexts, we designed and deployed FamilyBloom, a glanceable smartwatch and home display system for mood and goal tracking. Twelve families with both neurotypical and ADHD members used FamilyBloom for three months on average. Our findings reveal how family-centered tracking created collaboration opportunities and tensions across multiple ecological systems: individual self-regulation, collaborations within family dynamics, involvement of care networks with varying trust levels, institutional school constraints and cultural stigma, and temporality of regular routines and crisis periods. We discuss an ecosystem-aware approach to family informatics, wherein design can attend to how families navigate multiple contexts while sustaining family-level collaboration.

Family informatics often uses shared data dashboards to promote awareness of each other’s health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members are alone is also important, particularly to support parents exploring their co-regulation role, and assisting children with data interpretation and guidance on self and co-regulation. We discuss opportunities for displays to nurture multiple intents of use, such as joint or independent use, while potentially connecting with external expertise.

Challenges associated with ADHD affect children’s daily routines and response to environmental stimuli, and support from parents is helpful in managing and overcoming behavior regulation challenges. Positive reinforcement is increasingly integrated into family technologies for teaching regulation skills, but typically support specific co-located activities. To better understand how technology can support co-regulation within families with ADHD children, we deployed CoolTaco, a smartwatch and phone system to support collaboration in creating tasks, gaining points for achieving them, and redeeming rewards. Ten families with ADHD children used CoolTaco in their daily routines. By qualitatively analyzing family interviews and usage logs, we find that smartwatches can help provide pervasive regulation support to children, but the division across devices and parent-child roles interfere with developing independence. We discuss how technology should support co-regulation while also fostering future self-regulation, such as by guiding children in goal setting and helping them reflect on progress and achievements.

Children with ADHD can experience a wide variety of challenges related to self-regulation, which can lead to poor educational, health, and wellness outcomes. Technological interventions, such as mobile and wearable health systems, can support data collection and reflection about health status. However, little is known about how ADHD children interpret such data. We conducted a deployment study with 10 children, aged 10 to 15, for six weeks, during which they used a smartwatch in their homes. Results from observations and interviews during this study indicate that children with ADHD can interpret their own health data, particularly at the moment. However, as ADHD children develop more autonomy, smartwatch systems may require alternatives for data reflection that are interpretable and actionable for them. This work contributes to the scholarly discourse around health data visualization, particularly in considering implications for the design of health technologies for children with ADHD.

MOTIVATION: Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites. RESULTS: After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options. AVAILABILITY AND IMPLEMENTATION: Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative Structure-Activity Relationship modeling has paved the way toward the association of the physicochemical properties of peptides to their biological activity. Nowadays, there are algorithms that can compute thousands of physicochemical properties known as molecular descriptors. However, some of these descriptors are irrelevant and some might even mislead the correct classification of the peptide activity. To mitigate this problem, a descriptor selection process must be performed, this will help to improve the classification accuracy and to decrease the computational time required for classification. In a recent work, a general method to weight and select features has been proposed. The method models the descriptor selection problem as a multi-objective optimization problem (MOOP). The main idea is to optimize simultaneously the intra- and inter-class distances. We follow this approach and apply it to the feature selection problem for the classification of antimicrobial peptides. To this aim we modify the original MOOP formulation to avoid bringing together non-antimicrobial peptides. Preliminary results indicate that our approach can substantially reduce the number of required molecular descriptors and improve the performance of classification with respect to the original formulation.

Selective Cationic Antibacterial Peptides (SCAPs) are becoming a potential alternative to known antibiotics to deal with multi-drug resistant pathogens. Conventional techniques for the discovery and design of SCAPs can be time consuming and expensive, therefore the use of computers to aid such discovery or design is becoming attractive since they can help to reduce the number of sequences to be evaluated in the lab. A recent work shows that if a peptide fulfill a set of constraints on four physicochemical properties then the peptide is a candidate for being a SCAP. This result suggests that an exhaustive search can be used to discover new SCAPs. However, the size of the search space, 20n for peptides of length n, makes it prohibitively expensive to search for peptides longer than 11 amino acids on current desktop machines. As an alternative to the exhaustive search the present work proposes to model the SCAPs design problem as a multi-objective optimization problem. In order to solve the resulting problem an evolutionary algorithm based on the well known NSGA-II with a variable length representation and problem domain mutation operators is proposed. Preliminary results show that the approach can effectively and efficiently design SCAPs of different lengths. The predicted antibacterial peptides are validated in silico by using a publicly available Cationic Antimicrobial Peptides predictor.

We present Fermat, an Intelligent Social Network for Mathematics Learning, which integrates an Intelligent Tutoring System as an extra feature to help students to improve the teaching and learning process. The intelligent tutor takes into account both cognitive and affective aspects. The social network and the affective tutoring systems are accessed from the web. Initial results in math show the benefits of using the tutoring system besides the all-known benefits of using a social network as the user interface.