Dec 302015

Biosphere 2, the site of the final Phenotype RCN Summit meeting (February 2016). Photo (CC BY-NC 2.0) by pinkgranite. See original at

The Fifth Annual Summit of the Phenotype Ontology Research Coordination Network will be held at the University of Arizona’s Biosphere 2, about 40 miles north of Tucson, AZ, from February 26-28, 2016 (Friday through Sunday noon).

The theme of this meeting will be ‘Complex data integration with phenotypes’ with a focus on the integration of phenotype data with other data sets. We will summarize where our phenotype community is at with respect to integration with other data types, and we will highlight active projects. We will be looking to the future — what projects should be priorities for the future? Joining us this year will be folks from the newly funded ‘FuturePhy’ (, who are interested in how to integrate multiple data types, including phenotype, with phylogenetic trees.

We estimate that the costs for this meeting (transportation to meeting from airport, lodging, food) will be approximately $500, though we will be able to cover expenses for a small number of participants, particularly students and postdocs who have specific interests in using phenotypic data associated with environment in their research. Please contact one of us if you are interested in attending. It should be agreat meeting!

Paula Mabee;
Eva Huala;
Andy Deans;
Suzanna Lewis;

The Phenotype Ontology RCN ( was funded by the U.S. NSF to establish a network of scientists who are interested incomparing phenotypes across species and in developing the tools and methods needed to enable comparisons. In contrast to the many well-established efforts in the molecular community, the representation of phenotypic traits using ontologies is in its infancy. Phenotype ontologies, however, have the potential to integrate these data across all levels of the biological hierarchy and to the environment. This RCN is building a community that, because of its expertise, fosters communications across disciplines to enable co-development of interoperable community standards and best practices for phenotype.

 Posted by on December 30, 2015 at 1:50 am
Dec 212015

The following post is from Peter Midford. – Andy Deans

As you may recall, at a Spring 2013 meeting of the Phenotype RCN in Durham, NC, the Behavior Breakout group discussed the existence of multiple behavioral ontologies, including the gaps in existing ontologies (such as the Neuro Behavior Ontology, or NBO) that preclude their widespread use in behavioral ecology and other sub-disciplines in animal behavior. The group felt it could be possible to merge two existing behavioral ontologies – the NBO, developed to serve studies of animal models of human behavioral dysfunction, and the Animal Behavior Ontology or ABO, developed to serve the field of comparative animal behavior, including behavioral ecology and other sub-disciplines. If successful, the merger would facilitate the broader integration of behavioral studies: applied with basic, model organism with comparative investigations, mechanistic with evolutionary, and human with non-human animal questions. At the same time, it would also need to continue to serve the specialized needs of subfields.

In late summer 2014, a small group of animal behaviorists who were present at the 2013 meeting in Durham (Anne Clark, Sue Margulis, Peter Midford, Cynthia Parr) received NSF funding to hold two workshops to accomplish these goals.

Our first workshop, held August 2014 at Princeton University, convened over a dozen animal behaviorists with a broad range of expertise in comparative behavior to develop specific recommendations on how to integrate the basic terms and concepts of the two ontologies. Key outcomes included a list of proposed changes in parent-child relations in the NBO to emphasize function, and ABO term definition improvements that together could serve as the basis of integrating the two ontologies.

Our second workshop, supported in part by additional funding from the Phenotype RCN, was held at the Smithsonian’s National Museum of Natural History, Washington, DC, on October 24-25, 2015. Its specific goal was to start the process of merging the ABO and the NBO based on the first workshop’s recommendations. Attendees in addition to the four organizers, were our local host Katja Schultz (Encyclopedia of Life), Elissa Chesler (The Jackson Laboratory), George Gkoutos (NBO developer, University of Birmingham), David Osumi-Sutherland (European Bioinformatics Institute, Virtual Fly Brain), Melissa Haendel (Oregon Health and Science University), and Reid Rumelt (Cornell University undergraduate working with Macaulay Library and Encyclopedia of Life).

The workshop began with presentations about the histories of NBO and ABO. NBO had its roots in a phenotype vocabulary supporting the EUMORPHIA project (see and Behavior terms were initially included in the Gene Ontology, but also maps to phenotype ontologies, such as the Mammalian Phenotype ontology (MP) and Human Phenotype Ontology so as enable the integration of data. The Neuro Behavior Ontology was created to concentrate effort specifically on behavior.


ABO was one of the first accomplishments of the EthoSource project1, begun with an NSF-sponsored workshop in 2000 with the goal of developing integrated online resources for the discipline of Animal Behavior. Two NSF- sponsored Ontology Workshops followed in 2004-2005, at which an international group of animal behaviorists developed a basic metadata standard for the discipline, the ABO. The primary use of the ABO subsequent to 2005 was indexing an online ethogram repository,

In our second blog post, we will summarize the progress we made in the October workshop, and outline our next steps.

1Martins, E. P. 2004. EthoSource: Storing, Sharing, and Combining Behavioral Data. BioScience 54 (10): 886. doi:10.1641/0006-3568(2004)054[0886:ESSACB]2.0.CO;2

 Posted by on December 21, 2015 at 3:49 pm
Dec 182015

The following post is from Anne Thessen, who originally published this news on her blog, The Data Detektiv. – Andy Deans

One of the fundamental goals of biology is understanding the interactions of environment and phenotype, but this is a surprisingly difficult topic to study – not because of the concepts, but because of the data. Observations about environment and phenotype occur in separate data sets and the terms used are far too idiosyncratic for automated integration. Several biological domains, including conservation and phylogenetics could be advanced if these two data types could be easily merged on a large scale.

I led a recent paper, published in PeerJ, which suggests that the use of ontologies to standardize and link data about phenotypes and environments can enable scientific breakthroughs by increasing the scale and flexibility of research. This paper was a product of a workshop facilitated by the Phenotype RCN and supported by the National Science Foundation. My co-authors and I give several domain-specific use cases describing how an ontology can help advance science in four biological sciences. We then discuss the challenges to be addressed, present some proof-of-concept analyses, and discuss existing ontologies. The summary contains three suggestions for increasing interoperability between phenotype and environment data.

graphical illustration of the paper described in this blog

Graphical abstract for Thessen et al. (2015) DOI: 10.7717/peerj.1470. Click to enlarge.

We hope this paper provides you with an overview of the landscape of ontologies available for integrating environmental data, and inspires you to use them in relation to your own data. For more information about ontologies and semantics, a good first read is Semantic Web for the Working Ontologist by Dean Allemang and Jim Hendler.

 Posted by on December 18, 2015 at 2:27 am
Jul 272015
prickly plant leaf

Succulent plant with interesting adaptive phenotypes. Photo taken at the Rancho Santa Ana Botanic Garden (RSABG) by Manicosity (CC BY-ND 2.0). Click for original.

Supported by a Phenotype RCN collaboration grant, Grant Godden and Pier Luigi Buttigieg met during May 2015 at the Rancho Santa Ana Botanic Garden (RSABG) in Claremont, CA, with the aim of enhancing the ontological representation of plant environments. Grant and Pier processed label data from more than one million plant specimen records hosted by iDigBio, using a combination of natural language processing and text-mining techniques to identify well-represented terms and phrases in “habitat” descriptions. Their interactions with RSABG collections staff, whose active work with specimen digitization and insights into the creation of records that populate repositories like iDigBio, greatly enhanced the project and helped create a workable corpus. The preliminary results of the analyses were immediately informative, revealing gaps in the current coverage of the Environment Ontology (ENVO; Buttigieg et al., 2013).

Further work is planned to refine their computational pipeline and corpus, and to extend ENVO’s coverage of environments which the botanical community frequently sample. A brief publication reporting the process, findings, and results is in preparation.

GG is affiliated with the Rancho Santa Ana Botanic Garden, Claremont, CA, USA. PLB is affiliated with the Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany.

 Posted by on July 27, 2015 at 5:33 pm
Jun 102015

Visualizing one or more trees/taxonomies with non-trivial number of characters and taxa is a challenge a number of projects is facing. The ETC project organized a workshop with information visualization experts, data providers (trees and characters), and end users to tackle the challenge together.
The meeting was organized by Hong Cui and hosted by Bertram Ludäscher in the National Center for Supercomputing Applications (NCSA), Urbana, IL, in May 11-13. Phenotype RCN participants Matt Yoder, Nico Franz and Martín Ramírez attended the meeting and posed vis challenges. Much of the workshop was devoted to brainstorm on the challenge of representing a large dataset together with some kind of mapping on a tree, and often on two trees simultaneously. This is a familiar challenge for anatomy ontologists, who are trying to represent the interaction of phylogenetic trees, matrices and ontologies:

Right, the level of anatomical data available for different parts of the fin and limb can be visualized for taxa along the fin to limb transition (figure from Dececchi et al. in press 2015, Systematic Biology, doi: 10.1093/sysbio/syv031). Left, a phylogeny of spiders colored according to anatomical complexity, derived from the ontology (figure from Ramírez & Michalik 2014, doi: 10.1111/cla.12075).

Right, the level of anatomical data available for different parts of the fin and limb can be visualized for taxa along the fin to limb transition (figure from Dececchi et al. in press 2015, Systematic Biology, doi: 10.1093/sysbio/syv031). Left, a phylogeny of spiders colored according to anatomical complexity, derived from the ontology (figure from Ramírez & Michalik 2014, doi: 10.1111/cla.12075).

The beautiful and clever examples presented by the vis experts were inspiring. How these gorgeous examples can help us represent or complex data in intuitive visualizations? Filters, sort controls, heat maps, zoom panes, collapsing, expanding, and more tools – all made in us two effects: Make some of our challenges look feasible, and refine our vague ideas into more precise challenges.
Hierarchical Circular Layouts, or HCL (Dang, Murray, and Forbes 2015), uses circular layouts on a hierarchical structure.

Hierarchical Circular Layouts, or HCL (Dang, Murray, and Forbes 2015), uses circular layouts on a hierarchical structure.

PathwayMatrix: Visualizing Binary Relationships between Proteins in Biological Pathways (Dang, Murray, and Forbes, 2015). PathwayMatrix can be used not only for biological pathway visualization, but also for character and taxon data.  See:

PathwayMatrix: Visualizing Binary Relationships between Proteins in Biological Pathways (Dang, Murray, and Forbes, 2015). PathwayMatrix can be used not only for biological pathway visualization, but also for character and taxon data. See:

The ETC project will implement a few promising techniques as part of ETC toolkit to invite comments and suggestions from broader communities. Stay tuned, and crunch your data into nice visualizations!
(post by Martin Ramírez)

Jun 082015

CARO/PCO Oregon Summit 2014

Figure 1. SubClass hierarchy of upper-level classes in  the Common Anatomy Reference Ontology (CARO, orange boxes), plus relations to critical external ontology terms from Basic Formal Ontology (BFO, grey boxes), Population and Community Ontology (PCO, yellow box), and Gene Ontology (GO, pink boxes). Terms in light purple boxes are found in multiple ontologies (CL=Cell Ontology). The term ‘Organism’ in the green box is not an ontology term but a class from the Darwin Core Vocabulary that is a subclass of the new CARO term ‘biological entity’. ‘Biological entity’ is a catch-all term for any material entity that is, is part of, or derived from an organism, virus, or viroid, or a collection of them.

Figure 1. SubClass hierarchy of upper-level classes in the Common Anatomy Reference Ontology (CARO, orange boxes), plus relations to critical external ontology terms from Basic Formal Ontology (BFO, grey boxes), Population and Community Ontology (PCO, yellow box), and Gene Ontology (GO, pink boxes). Terms in light purple boxes are found in multiple ontologies (CL=Cell Ontology). The term ‘Organism’ in the green box is not an ontology term but a class from the Darwin Core Vocabulary that is a subclass of the new CARO term ‘biological entity’. ‘Biological entity’ is a catch-all term for any material entity that is, is part of, or derived from an organism, virus, or viroid, or a collection of them.

Before the post-Thanksgiving haze had lifted, a small group of ontologists (Melissa Haendel, Chris Mungall, David Osumi-Sutherland, and Ramona Walls) converged on the lovely small town of Brownsville, Oregon to work on the Common Anatomy Reference Ontology (CARO), the Population and Community Ontology (PCO), and PATO, an ontology of biological qualities. This work was done within the context of the larger group of ontologies that make use of or are used by CARO (UBERON, GO, CL).

CARO is a relatively small upper ontology with ~165 classes and a few core relations that is used to link taxon-specific anatomy ontologies ranging from fruit flies to vertebrates to plants. The 1.0 release of CARO has been widely used, but usage has been quite inconsistent and sometimes incorrect. This is partly due to lack of clarity in some definitions, but also because it was written at a time when we lacked the tools to provide automated reports of incorrect usage.

PCO is recently developed ontology focussing on populations, communities and the relationships between organisms.  The definitions of organism types in CARO are critically important for this ontology, as are the biological qualities applying to groups of organisms in PATO.

PATO, an ontology of biological qualities, has been very widely used by the community brought together by the Phenotype RCN as well as in defining classes in a wide range of other ontologies used by this community (covering phenotypes, anatomy, cell types and populations).  So far, PATO has had limited axiomatisation, but there many obvious cases where axiomatisation could improve its integration with ontologies that use it – including the PCO and anatomy ontologies.

A major aim of our work on CARO at this meeting was to redraft textual definitions so that they could be understood by any competent biologist and to redraft logical definitions so that they could be used for automated classification and error checking. For both logical and textual definitions, we aimed to focus on distinctions that are important to biologists – either directly, or indirectly by making biologically useful queries possible.  We also aimed to take into account new use cases that have arisen since CARO 1.0 was released, as a result of work on the PCO as well as on anatomy ontologies and the ontologies and tools that use them.  In parallel with this work, we aimed to improve related axiomatisation of PATO.

Over two and half days of leftover turkey, home-fermented vegetables, and farm-fresh eggs, we took care of operational issues such as repository maintenance, as well as more hard-core ontologizing. A highlight of the meeting was an informal gathering on Monday night when we were joined by Laurel Cooper and John Campbell from Oregon State University and Joe Fontaine from Murdoch University to discuss the intersections of ontologies, ecology, plant traits, and biodiversity.

Key outputs of the meeting were:

  • A fresh github repo ( for CARO, with cleaned up imports.
  • New CARO terms, including terms for multicellular anatomical structure and expression pattern, and a general term for organs.
  • Revised text and logical definitions for most CARO terms, including anatomical structure, cellular organism, and organ (figure 1, Vue file that shows the key classes and which files they live in).
  • Draft ontology design patterns (ODPs) for expression patterns and for anatomical structures with internal spaces (lumens).
  • Further development of PCO, including updating import files, testing ODPs for defining collections of organisms and species/organism interactions.
  • A pending beta release of CARO2.0 and plans for how to announce it.
  • Better formalization of PATO through general class axioms (GCIs) necessary for CARO and PCO.
  • A Jenkins job that reports on and verifies ontologies that use CARO (FBBT, PO, XAO, and ZFA))
  • A draft paper on CARO2.0.

One of the key use cases for anatomy ontologies is annotation of gene expression, and we wanted a way to help curators avoid the pitfall of annotating expression to the (immaterial) space that is part of a structure rather than the (material) structure that surrounds it. We propose a design pattern in which any structure that has an interior space (such as stomach) would be modeled using four classes: one for the entire structure (which includes both the surrounding structure and the space that is part of it), one for the space, one for the wall (which is just the surrounding structure without the space) and one for “wall region”. A wall region is any portion of the wall that spans the full thickness of the wall for its entire lateral extent, whereas the wall is the mereotopological sum of all wall regions. Following this pattern, an ontology that wished to include a stomach would have classes for “stomach”, “stomach lumen”, “stomach wall”, and “region of stomach wall”. We opted against including very general classes such as “wall” or “wall region” in CARO, and instead plan to document the pattern and provide a template for its use in anatomy ontologies.

One way of specifying the structures such as a stomach that have a geometric component is through the use of GCIs in PATO. PATO includes a number of classes for qualities describing shape. Of these, lumenized, tubular, and saccular are the most relevant to CARO. We began adding GCIs to PATO of the form:

  • bearer_of some lumenized subClassOf ‘has part’ some lumen
  • bearer_of some unlumenized subClassOf not (‘has part’ some lumen)

An open question remains on how to document these patterns (in CARO or as separate patterns). One possibility is for CARO to include abstract geometrical classes such as “anatomical tube” or “anatomical tube wall” and “tube lumen”.

Stay tuned for another post soon, with the upcoming release of CARO!

May 272015

How do phenotypic data factor into the issues relating to integrating complex data? Three frequent phenotypers (Ramona Walls, Chris Mungall, and Maryann Martone) were supported by this RCN to participate with sixteen others in an ‘Integrating Complex Data’ workshop organized by the American Institute for Biological Sciences (AIBS) with NSF funding (EF-1450894), on March 30-31 at the Hyatt Regency Crystal City in Arlington, Virginia. The workshop was co-chaired by Paula Mabee, Corinna Gries, and Robert Gropp, facilitated by Kathy Joyce, and observed by various program officers and staff from NSF.

Complex data integration, defined as ‘bringing together data from two or more fields’, is required to address many fundamental scientific questions as well as understanding how to mitigate the challenges facing the planet. Participants (whose research interests ranged from genetics, genomics, metagenomics, systematics, taxonomy, and ecology, to bio/eco-informatics and cyberinfrastructure development) initially discussed specific use cases in which complex data integration was required. They then focused on the barriers that impede integration, recognizing domain silos as major problem at this scale. They illustrated with examples that data discovery and integration are currently hampered by lack of common standards, including those for IDs, representation, ontologies, data formats, data collection, and communication protocols.  The usefulness of ontologies in connecting phenotypic data to other data types across domains was described by Phenotype RCN participants.

Suggestions and next steps required to achieve better data integration were the focus of the second day of the workshop. Community coalescence around shared standards, rather than more standards, was considered key.  Participants advocated for interagency discussions about how to provide linkages across their data systems, thus making data from all sites more readily discoverable and distributing the financial burden.  Participants further recognized that the technical expertise required for complex data integration is high; they promoted cross-training in informatics for graduate students and a higher level of specialist ‘data scientist’ training.  They also felt that funding mechanisms to enable scientists to employ technical specialists for specific data integration tasks would enable complex data integration.  Particularly at this juncture, where cross-domain data analysis is required to address societal problems, participants stressed that it is important to try to solve the immediate problems while working toward long-range solutions.  A full report from this workshop is in preparation and a link will be posted when it is available.

 Posted by on May 27, 2015 at 11:55 pm
Apr 212015

Photo used under Creative Commons from Kirt Edblom.

The Alfred P. Sloan Foundation’s Digital Information Technology program has awarded $499K to Phoenix Bioinformatics to catalyze development of creative new user-based funding strategies for research databases.   Phoenix was founded in 2013 by the staff of the Arabidopsis Information Resource (TAIR) to provide new support mechanisms as TAIR transitioned away from grant-based funding. Following its success with TAIR, Phoenix will be assisting other databases with their funding challenges and helping them find new ways to sustain their projects for the long term with community help. For more information about TAIR’s transition to sustainable funding or the newly funded Phoenix project please contact Phoenix Bioinformatics.

Phoenix Bioinformatics is a nonprofit 501(c)3 organization dedicated to finding innovative ways to sustain critical scientific resources.

 Posted by on April 21, 2015 at 7:35 pm
Jan 092015

Figure assembled by Anya Broverman-Wray (CC BY 2.0) doi: 10.1371/journal.pbio.1002033.g001

In case you missed it, our latest Phenotype RCN publication came out this week in PLoS Biology. In this perspective we argue for more investment in the infrastructure needed to make phenotypes more accessible. Check it out!

Abstract.—Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today’s data barriers and facilitate analytical reproducibility.

Deans AR, Lewis SE, Huala E, Anzaldo SS, Ashburner M, Balhoff JP, Blackburn DC, Blake JA, Burleigh JG, Chanet B, Cooper LD, Courtot M, Csösz S, Cui H, Dahdul W, Das S, Dececchi TA, Dettai A, Diogo R, Druzinsky RE, Dumontier M, Franz NM, Friedrich F, Gkoutos GV, Haendel M, Harmon LJ, Hayamizu TF, He Y, Hines HM, Ibrahim N, Jackson LM, Jaiswal P, James-Zorn C, Köhler S, Lecointre G, Lapp H, Lawrence CJ, Le Novère N, Lundberg JG, Macklin J, Mast AR, Midford PE, Mikó I, Mungall CJ, Oellrich A, Osumi-Sutherland D, Helen Parkinson, Ramírez MJ, Richter S, Robinson PN, Ruttenberg A, Schulz KS, Segerdell E, Seltmann KC, Sharkey MJ, Smith AD, Smith B, Specht CD, Squires RB, Thacker RW, Thessen A, Fernandez-Triana J, Vihinen M, Vize PD, Vogt L, Wall CE, Walls RL, Westerfeld M, Wharton RA, Wirkner CS, Woolley JB, Yoder MJ, Zorn AM, Mabee PM. (2015) Finding our way through phenotypes. PLoS Biology 13(1): e1002033. DOI: 10.1371/journal.pbio.1002033

Dec 072014

euroevodevoVienna2014In late July, the Phenotype RCN and Phenoscape co-sponsored several speakers in the symposium “What should Bioinformatics do for EvoDevo?” co-organized by Günter Plickert, Mark Blaxter, Paula Mabee and Ann Burke. The symposium was part of the European Society for Evolutionary Developmental Biology (EED) meeting, held in Vienna. The organizers brought together speakers whose research and perspectives provided examples of how EvoDevo data integration is necessary for discoveries.  Several speakers presented new insights into EvoDevo that were directly derived from sequencing genomes or transcriptomes.   Others showed how by using semantic methods to represent species phenotypes, they could be linked to genetic and developmental data, and the research questions that they addressed. This well-attended symposium met its goals, which were to:

  • promote awareness of new and developing resources and methods as well as EvoDevo uses of existing ones.
  • promote discussions in the EvoDevo community that value input of bioinformatics to EvoDevo questions.
  • invite the audience to share their ideas of how to move the integration forward

The excellent organization of this conference and the wonderful venue helped spark several new collaborations and grant proposals.  Talks and speakers in this symposium included (full program found here):

  1. Bioinformatics for EvoDevo: Connecting evolutionary morphology and model organism genetics, presented by Paula Mabee (University of South Dakota, Vermillion, SD, USA)
  2. Insights into the evolution and development of planarian regeneration from the genome of the flatworm, Girardia tigrina, presented by Sujai Kumar (University of Oxford, GBR)
  3. From the wet lab to the computer and back: A stage specific RNAseq analysis elucidates the molecular underpinnings and evolution of Hydrozoan development, presented by Philipp Schiffer (University of Cologne, GER)
  4. Insights into the evolution of early development of parthenogenetic nematodes by second generation sequencing, presented by Christopher Kraus (University of Cologne, GER)
  5. Petaloidy, polarity and pollination: The evolution of organ morphology networks, presented by Chelsea Specht (University of California Berkeley, CA, USA)
  6. Aligning phonemes and genomes to understand the evolution of multicellular organisms, presented by Philip Donoghue (University of Bristol, GBR)
  7. Online databases provide critical insights into the evolution of appendage modularity during the fin to limb transition, presented by Karen Sears (University of Illinois, Urbana, IL, USA)
  8. Evolutionally conserved mechanisms of regeneration in chordates: Uncovering pathways active during WBR in Botrylloides leachi, presented by Lisa Zondag (University of Otago, Dunedin, NZL)
  9. Phylogenomics of MADS-box genes in flowering plants to identify EvoDevo genes, presented by Guenter Theissen (Friedrich Schiller University Jena, GER)
  10. Illuminating the evolutionary origin of the turtle shell by a comparative tissue-specific transcriptome analysis, presented by Juan Pascual-Anaya (RIKEN Center for Developmental Biology, Kobe, JPN)
  11. Blastodermal segmentation in the milkweed bug, Oncopeltus facsiatus, presented by Ariel Chipman (The Hebrew University of Jerusalem, ISR)
  12. The origins of arthropod innovations: Insights from the noninsect arthropods, the cherry shrimp and rusty millipede, presented by Nathan Kenny (The Chinese University of Hong Kong, HKG)
 Posted by on December 7, 2014 at 4:52 pm