An improved ontological representation of dendritic cells as a paradigm for all cell types

The Cell Ontology (CL) is designed to provide a standardized representation of cell types for data annotation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL’s utility for cross-species data integration. To address this problem, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. 104. Barry Smith, “Toward a Realistic Science of Environments”, Ecological Psychology, 2009, 21 (2), April-June, 121-130. Abstract: The perceptual psychologist J. J. Gibson embraces a radically externalistic view of mind and action. We have, for Gibson, not a Cartesian mind or soul, with its interior theater of contents and the consequent problem of explaining how this mind or soul and its psychological environment can succeed in grasping physical objects external to itself. Rather, we have a perceiving, acting organism, whose perceptions and actions are always already tuned to the parts and moments, the things and surfaces, of its external environment. We describe how on this basis Gibson sought to develop a realist science of environments which will be ‘consistent with physics, mechanics, optics, acoustics, and chemistry’

iWAS – A novel approach to analyzing Next Generation Sequence data for immunology

In this communication we describe a novel way to use Next Generation Sequence from the receptors expressed on T and B cells. This informatics methodology is named iWAS, for immunonome Wide Association Study, where we use the immune receptor sequences derived from T and B cells and the features of those receptors (sequences themselves, V/J gene usage, length and character each of the CDR3 sub-regions) to define biomarkers of health and disease, as well as responses to therapies. Unlike GWAS, which do not provide immediate access to mechanism, the associations with immune receptors immediately suggest possible and plausible entrée's into disease pathogenesis and treatment

Associations between Prenatal Exposure to Black Carbon and Memory Domains in Urban Children: Modification by Sex and Prenatal Stress

Background Whether fetal neurodevelopment is disrupted by traffic-related air pollution is uncertain. Animal studies suggest that chemical and non-chemical stressors interact to impact neurodevelopment, and that this association is further modified by sex. Objectives To examine associations between prenatal traffic-related black carbon exposure, prenatal stress, and sex with children’s memory and learning. Methods Analyses included N = 258 mother-child dyads enrolled in a Boston, Massachusetts pregnancy cohort. Black carbon exposure was estimated using a validated spatiotemporal land-use regression model. Prenatal stress was measured using the Crisis in Family Systems-Revised survey of negative life events. The Wide Range Assessment of Memory and Learning (WRAML2) was administered at age 6 years; outcomes included the General Memory Index and its component indices [Verbal, Visual, and Attention Concentration]. Relationships between black carbon and WRAML2 index scores were examined using multivariable-adjusted linear regression including effect modification by stress and sex. Results Mothers were primarily minorities (60% Hispanic, 26% Black); 67% had ≤12 years of education. The main effect for black carbon was not significant for any WRAML2 index; however, in stratified analyses, among boys with high exposure to prenatal stress, Attention Concentration Index scores were on average 9.5 points lower for those with high compared to low prenatal black carbon exposure (P3-way interaction = 0.04). Conclusion The associations between prenatal exposure to black carbon and stress with children’s memory scores were stronger in boys than in girls. Studies assessing complex interactions may more fully characterize health risks and, in particular, identify vulnerable subgroups

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
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An improved ontological representation of dendritic cells as a paradigm for all cell types

<p>Abstract</p> <p>Background</p> <p>Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple <it>is_a </it>relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL's utility for computation and for cross-species data integration.</p> <p>Results</p> <p>To enhance the CL's utility for computational analyses, we developed a method for the ontological representation of cells and applied this method to develop a dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. We avoid multiple uses of <it>is_a </it>by linking DC-CL terms to terms in other ontologies via additional, formally defined relations such as <it>has_function</it>.</p> <p>Conclusion</p> <p>This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. Accordingly, we propose our method as a general strategy for the ontological representation of cells. DC-CL is available from <url>http://www.obofoundry.org</url>.</p

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented

Can early weight loss, eating behaviors and socioeconomic factors predict successful weight loss at 12- and 24-months in adolescents with obesity and insulin resistance participating in a randomised controlled trial?

© 2016 Gow et al.Background: Lifestyle interventions in adolescents with obesity can result in weight loss following active intervention but individual responses vary widely. This study aimed to identify predictors of weight loss at 12- and 24-months in adolescents with obesity and clinical features of insulin resistance. Methods: Adolescents (n = 111, 66 girls, aged 10-17 years) were participants in a randomised controlled trial, the RESIST study, examining the effects of two diets differing in macronutrient content on insulin sensitivity. Eighty-five completed the 12-month program and 24-month follow-up data were available for 42 adolescents. Change in weight was determined by BMI expressed as a percentage of the 95th percentile (BMI95). The study physician collected socioeconomic data at baseline. Physical activity and screen time, and psychological dimensions of eating behavior were self-reported using the validated CLASS and EPI-C questionnaires, respectively. Stepwise multiple regressions were conducted to identify models that best predicted change in BMI95 at 12- and 24-months. Results: Mean BMI95 was reduced at 12-months compared with baseline (mean difference [MD] ± SE: -6.9 ± 1.0, P < 0.001) but adolescents had significant re-gain from 12- to 24-months (MD ± SE: 3.7 ± 1.5, P = 0.017). Participants who achieved greater 12-month weight loss had: greater 3-month weight loss, a father with a higher education, lower baseline external eating and parental pressure to eat scores and two parents living at home. Participants who achieved greater 24-month weight loss had: greater 12-month weight loss and a lower baseline emotional eating score. Conclusions: Early weight loss is consistently identified as a strong predictor of long-term weight loss. This could be because early weight loss identifies those more motivated and engaged individuals. Patients who have baseline factors predictive of long-term weight loss failure may benefit from additional support during the intervention. Additionally, if a patient does not achieve early weight loss, further support or transition to an alternate intervention where they may have increased success may be considered. Trial registration: Australian New Zealand Clinical Trial Registration Number (ACTRN) 12608000416392 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=83071Link_to_subscribed_fulltex

Instructions to BBF RFC Authors

This BioBricks Foundation Request for Comments (BBF RFC) provides in- formation about the preparation and submission of BBF RFCs to The Bio- Bricks Foundation (BBF)

VO: Vaccine Ontology

The collaborative, community-based Vaccine Ontology (VO) was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Currently VO covers a variety of aspects of the vaccine domain, with an emphasis on classification of vaccines and vaccine components, and on host immune response to vaccines. VO can be used for a number of applications, e.g., ontology-based vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI)

Bioelectrical impedance analysis to estimate body composition, and change in adiposity, in overweight and obese adolescents: Comparison with dual-energy x-ray absorptiometry

© 2014 Wan et al.; licensee BioMed Central Ltd.Background: There is a need for a practical, inexpensive method to assess body composition in obese adolescents. This study aimed to 1) compare body composition parameters estimated by a stand-on, multi-frequency bioelectrical impendence (BIA) device, using a) the manufacturers' equations, and b) published and derived equations with body composition measured by dual-energy x-ray absorptiometry (DXA) and 2) assess percentage body fat (%BF) change after a weight loss intervention.Methods: Participants were 66 obese adolescents, mean age (SD) 12.9 (2.0) years. Body composition was measured by Tanita BIA MC-180MA (Tanita BIA8) and DXA (GE-Lunar Prodigy). BIA resistance and reactance data at frequencies of 5, 50, 250 and 500 kHz, were used in published equations, and to generate a new prediction equation for fat-free mass (FFM) using a split-sample method. Approximately half (n = 34) of the adolescents had their body composition measured by DXA and BIA on two occasions, three to nine months apart.Results: The correlations between FFM (kg), fat mass (kg) and %BF measured by BIA and DXA were 0.92, 0.93 and 0.78, respectively. The Tanita BIA8 manufacturers equations significantly (P < 0.001) overestimated FFM (4.3 kg [-5.3 to 13.9]) and underestimated %BF (-5.0% [-15 to 5.0]) compared to DXA. The mean differences between BIA derived equations and DXA measured body composition parameters were small (0.4 to 2.1%), not significant, but had large limits of agreements (~ ±15% for FFM). After the intervention mean %BF loss was similar by both methods (~1.5%), but with wide limits of agreement.Conclusion: The Tanita BIA8 could be a valuable clinical tool to measure body composition at the group level, but is inaccurate for the individual obese adolescent.Link_to_subscribed_fulltex