Bridging Community, History, and Culture in Personal Informatics Tools

A healthy diet and increased physical activity are essential for reducing the prevalence of cardiovascular disease and related deaths, a worldwide public health concern that disproportionately affects Black American communities. Still, Black Americans can face unique challenges meeting dietary and physical activity requirements due to inequities in access and quality of care, environmental and local factors, and difficulties in changing individual health behaviors. Personal informatics and self-tracking tools are one way of increasing awareness of health behaviors to motivate behavior change. However, there are still gaps in knowledge about what encourages different users to engage with personal informatics tools over time, particularly when used in collaborative, community-health settings. This paper contributes a nuanced understanding of fifteen participants' reasons for engaging in an existing community-based health education and behavior change program that combines collaborative self-tracking with culturally relevant content and social engagement to motivate heart-healthy behaviors. We illustrate participants' positive and negative experiences engaging in self-tracking and collaborative tasks during the program. We also discuss how participants envision that integrating technology might support or hinder participant engagement and the work of deploying community-based public health interventions. Finally, we discuss design implications for culturally informed, community-based personal informatics tools that engage Black American's in heart-healthy activities.

Mathematical Analysis and Update of ADM1 Model for Biomethane Production by Anaerobic Digestion

Biomethane is a renewable product that can directly substitute its fossil counterpart, although its synthesis from residual biomasses has some hurdles. Because of the complex nature of both biomasses and the microbial consortia involved, innovative approaches such as mathematical modeling can be deployed to support possible improvements. The goal of this study is two-fold, as we aimed to modify a part of the Anaerobic Digestion Model No. 1 (ADM1), describing biomethane production from activated sludge, matching with its actual microbial nature, and to use the model for identifying relevant parameters to improve biomethane production. Firstly, thermodynamic analysis was performed, highlighting the direct route from glucose to biomethane as the most favorable. Then, by using MATLAB® and Simulink Toolbox, we discovered that the model fails to predict the microbiological behavior of the system. The structure of the ADM1 model was then modified by adding substrate consumption yields in equations describing microbial growth, to better reflect the consortium behavior. The updated model was tested by modifying several parameters: the coefficient of decomposition was identified to increase biomethane production. Approaching mathematical models from a microbiological point of view can lead to further improvement of the models themselves. Furthermore, this work represents additional evidence of the importance of informatics tools, such as bioprocess simulations to foster biomethane role in bioeconomy.

Cloning, expression, purification and functional analysis of a specific multi-epitope protein from multi drug resistance Acinetobacter baumannii

Background and Objectives: Immunization is a promising strategy to combat against the life-threatening infections by Multi Drug Resistance Acinetobacter baumannii. In this study, we directed to design and evaluate the efficacy of a recombi- nant multi-epitope protein against this pathogen. Materials and Methods: Epitopes prediction was performed for candidate proteins OmpA and BAM complex (BamA, BamB, BamC, BamD, BamE) from A. baumannii, using immune-informatics tools with high affinity for the human HLA alleles. After expression and purification of the recombinant protein, its functional activity was confirmed by interaction with positive sera. Results: Cloning and expression of the desired multi-epitopes protein were verified. Circular Dichroism study showed the secondary structure and proper refolding of the recombinant protein was achieved and matched with computational predic- tion. There was a significant interaction between designed protein with antibodies presented in ICU patients' and staff's sera. Conclusion: The interaction of the recombinant protein with patients' sera antibodies suggests that it may be a promising determinant protein for immunization against of MDR A. baumannii.

Memory mapped parallel BAM file access API for high throughput sequence analysis informatics

In precision medicine, genomic data needs to be processed as fast as possible to arrive at treatment decisions in a timely fashion. We developed mmbam, a library to allow sequence analysis informatics software to access raw sequencing data stored in BAM files extremely fast. Taking advantage of memory mapped file access and parallel data processing, we demonstrate that analysis software ported to mmbam consistently outperforms their stock versions. Open source and freely available, we envision that mmbam will enable a new generation of high performance informatics tools for precision medicine.

Leveraging Clinical Informatics Tools to Extract Cumulative Anthracycline Exposure, Measure Cardiovascular Outcomes, and Assess Guideline Adherence for Children With Cancer

PURPOSE Cardiovascular disease is a significant cause of late morbidity and mortality in survivors of childhood cancer. Clinical informatics tools could enhance provider adherence to echocardiogram guidelines for early detection of late-onset cardiomyopathy. METHODS Cancer registry data were linked to electronic health record data. Structured query language facilitated the construction of anthracycline-exposed cohorts at a single institution. Primary outcomes included the data quality from automatic anthracycline extraction, sensitivity of International Classification of Disease coding for heart failure, and adherence to echocardiogram guideline recommendations. RESULTS The final analytic cohort included 385 pediatric oncology patients diagnosed between July 1, 2013, and December 31, 2018, among whom 194 were classified as no anthracycline exposure, 143 had low anthracycline exposure (< 250 mg/m2), and 48 had high anthracycline exposure (≥ 250 mg/m2). Manual review of anthracycline exposure was highly concordant (95%) with the automatic extraction. Among the unexposed group, 15% had an anthracycline administered at an outside institution not captured by standard query language coding. Manual review of echocardiogram parameters and clinic notes yielded a sensitivity of 75%, specificity of 98%, and positive predictive value of 68% for International Classification of Disease coding of heart failure. For patients with anthracycline exposure, 78.5% (n = 62) were adherent to guideline recommendations for echocardiogram surveillance. There were significant association with provider adherence and race and ethnicity ( P = .047), and 50% of patients with Spanish as their primary language were adherent compared with 90% of patients with English as their primary language ( P = .003). CONCLUSION Extraction of treatment exposures from the electronic health record through clinical informatics and integration with cancer registry data represents a feasible approach to assess cardiovascular disease outcomes and adherence to guideline recommendations for survivors.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Beyond self-reflection: introducing the concept of rumination in personal informatics

Personal informatics tools can help users self-reflect on their experiences. When reflective thought occurs, it sometimes leads to negative thought and emotion cycles. To help explain these cycles, we draw from Psychology to introduce the concept of rumination—anxious, perseverative cognition focused on negative aspects of the self—as a result of engaging with personal data. Rumination is an important concept for the Human Computer Interaction community because it can negatively affect users’ well-being and lead to maladaptive use. Thus, preventing and mitigating rumination is beneficial. In this conceptual paper, we differentiate reflection from rumination. We also explain how self-tracking technologies may inadvertently lead to rumination and the implications this has for design. Our goal is to expand self-tracking research by discussing these negative cycles and encourage researchers to consider rumination when studying, designing, and promoting tools to prevent adverse unintended consequences among users.