Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics.

The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multispecies anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to: 1) use controlled vocabularies and create semiautomated computer-parsable insect morphological descriptions; 2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and 3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.

Transforming the study of organisms: Phenomic data models and knowledge bases.

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

Towards a semantic approach to numerical tree inference in phylogenetics.

Conventional approaches to phylogeny reconstruction require a character analysis step prior to and methodologically separated from a numerical tree inference step. The former results in a character matrix that contains the empirical data analysed in the latter. This separation of steps involves various methodological and conceptual problems (e.g. homology assessment independent of tree inference and character optimization, character dependencies, discounting of alternative homology hypotheses). In morphology, the character analysis step covers the stages of morphological comparative studies, homology assessment and the identification and coding of morphological characters. Unfortunately, only the last stage requires some formalism, whereas the preceding stages are commonly regarded to be pre-rational and intuitive, which is why their reproducibility and analytical accessibility is limited. Here, I introduce a rational for a semantic approach to numerical tree inference that uses sets of semantic instance anatomies as data source instead of character matrices, thereby avoiding the above-mentioned problems. A semantic instance anatomy is an ontology-based description of the anatomical organization of a specimen in the form of a semantic graph. The semantic approach to numerical tree inference combines and integrates the steps of character analysis and numerical tree inference and makes both analytically accessible and communicable. Before outlining first steps for a research programme dedicated to the semantic approach to numerical tree inference, I discuss in detail the methodological, conceptual, and computational challenges and requirements that first have to be dealt with before adequate algorithms can be developed.

The logical basis for coding ontologically dependent characters.

The coding of dependent morphological characters represents a major methodological problem in phylogenetics. Based on a distinction of semantic and ontological logical character dependency, I suggest how inapplicables can be treated properly and introduce rules of mutually dependent character states, which specify how character states of one character determine character states in its ontologically dependent characters. Using various examples, I discuss a set of general rules that applies independently of whether the ontological dependency results from property instantiation, parthood or subsumption. When implemented in a matrix editor, these rules would significantly facilitate the coding procedure, speed up coding of large matrices and increase overall consistency. If implemented in algorithms for tree reconstruction, the rules would prevent inconsistent reconstructions of ancestral states, which is a common problem with inapplicables. In the second part of the paper I set out to explore the potential of using a semantic framework and semantic techniques for automatically detecting instances of ontological dependency and specific cases of semantic dependency and how they can be applied for automatically coding character state values for ontologically dependent characters using the general rules discussed in the first part of the paper. This approach utilizes graph-based semantic representations of instance anatomy, which represent ontology-based descriptions of the anatomical organization of individual organisms.

Assessing similarity: on homology, characters and the need for a semantic approach to non-evolutionary comparative homology.

The problem of homology has been a consistent source of controversy at the heart of systematic biology, as has the step of morphological character analysis in phylogenetics. Based on a clear epistemic framework and a characterization of "characters" as diagnostic evidence units for the recognition of not directly identifiable entities, I discuss the ontological definition and empirical recognition criteria of phylogenetic, developmental and comparative homology, and how these three accounts of homology each contribute to an understanding of the overall phenomenon of homology. I argue that phylogenetic homologies are individuals or historical kinds that require comparative homology for identification. Developmental homologies are natural kinds that ultimately rest on phylogenetic homologies and also require comparative homology for identification. Comparative homologies on the other hand are anatomical structural kinds that are directly identifiable. I discuss pre-Darwinian comparative homology concepts and their problem of invoking non-material forces and involving the a priori assumption of a stable positional reference system. Based on Young's concept of comparative homology, I suggest a procedure for recognizing comparative homologues that lacks these problems and that utilizes a semantic framework. This formal conceptual framework provides the much needed semantic transparency and computer-parsability for documenting, communicating and analysing similarity propositions. It provides an essential methodological framework for generalizing over individual organisms and identifying and demarcating anatomical structural kinds, and it provides the missing link to the logical chain of identifying phylogenetic homology. The approach substantially increases the analytical accessibility of comparative research and thus represents an important contribution to the theoretical and methodological foundation of morphology and comparative biology.

Semantic units: organizing knowledge graphs into semantically meaningful units of representation.

BACKGROUND: In today's landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles-ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. RESULTS: We introduce "semantic units" as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. CONCLUSIONS: Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research.

BACKGROUND: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? QUESTIONS: Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. RESULTS: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information-a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. CONCLUSIONS: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example.

BACKGROUND: The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. RESULTS: Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. CONCLUSIONS: We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.

Spatio-structural granularity of biological material entities.

BACKGROUND: With the continuously increasing demands on knowledge- and data-management that databases have to meet, ontologies and the theories of granularity they use become more and more important. Unfortunately, currently used theories and schemes of granularity unnecessarily limit the performance of ontologies due to two shortcomings: (i) they do not allow the integration of multiple granularity perspectives into one granularity framework; (ii) they are not applicable to cumulative-constitutively organized material entities, which cover most of the biomedical material entities. RESULTS: The above mentioned shortcomings are responsible for the major inconsistencies in currently used spatio-structural granularity schemes. By using the Basic Formal Ontology (BFO) as a top-level ontology and Keet's general theory of granularity, a granularity framework is presented that is applicable to cumulative-constitutively organized material entities. It provides a scheme for granulating complex material entities into their constitutive and regional parts by integrating various compositional and spatial granularity perspectives. Within a scale dependent resolution perspective, it even allows distinguishing different types of representations of the same material entity. Within other scale dependent perspectives, which are based on specific types of measurements (e.g. weight, volume, etc.), the possibility of organizing instances of material entities independent of their parthood relations and only according to increasing measures is provided as well. All granularity perspectives are connected to one another through overcrossing granularity levels, together forming an integrated whole that uses the compositional object perspective as an integrating backbone. This granularity framework allows to consistently assign structural granularity values to all different types of material entities. CONCLUSIONS: The here presented framework provides a spatio-structural granularity framework for all domain reference ontologies that model cumulative-constitutively organized material entities. With its multi-perspectives approach it allows querying an ontology stored in a database at one's own desired different levels of detail: The contents of a database can be organized according to diverse granularity perspectives, which in their turn provide different views on its content (i.e. data, knowledge), each organized into different levels of detail.

Invertebrate neurophylogeny: suggested terms and definitions for a neuroanatomical glossary.

BACKGROUND: Invertebrate nervous systems are highly disparate between different taxa. This is reflected in the terminology used to describe them, which is very rich and often confusing. Even very general terms such as 'brain', 'nerve', and 'eye' have been used in various ways in the different animal groups, but no consensus on the exact meaning exists. This impedes our understanding of the architecture of the invertebrate nervous system in general and of evolutionary transformations of nervous system characters between different taxa. RESULTS: We provide a glossary of invertebrate neuroanatomical terms with a precise and consistent terminology, taxon-independent and free of homology assumptions. This terminology is intended to form a basis for new morphological descriptions. A total of 47 terms are defined. Each entry consists of a definition, discouraged terms, and a background/comment section. CONCLUSIONS: The use of our revised neuroanatomical terminology in any new descriptions of the anatomy of invertebrate nervous systems will improve the comparability of this organ system and its substructures between the various taxa, and finally even lead to better and more robust homology hypotheses.

The linguistic problem of morphology: structure versus homology and the standardization of morphological data.

The present article discusses the need for standardization in morphology in order to increase comparability and communicability of morphological data. We analyse why only morphological descriptions and not character matrices represent morphological data and why morphological terminology must be free of homology assumptions. We discuss why images only support and substantiate data but are not data themselves. By comparing morphological traits and DNA sequence data we reveal fundamental conceptual shortcomings of the former that result from their high average degree of individuality. We argue that the delimitation of morphological units, of datum units, and of evidence units must be distinguished, each of which involves its own specific problems. We conclude that morphology suffers from the linguistic problem of morphology that results from the lack of (i) a commonly accepted standardized morphological terminology, (ii) a commonly accepted standardized and formalized method of description, and (iii) a rationale for the delimitation of morphological traits. Although this is not problematic for standardizing metadata, it hinders standardizing morphological data. We provide the foundation for a solution to the linguistic problem of morphology, which is based on a morphological structure concept. We argue that this structure concept can be represented with knowledge representation languages such as the resource description framework (RDF) and that it can be applied for morphological descriptions. We conclude with a discussion of how online databases can improve morphological data documentation and how a controlled and formalized morphological vocabulary, i.e. a morphological RDF ontology, if it is based on a structure concept, can provide a possible solution to the linguistic problem of morphology. © The Willi Hennig Society 2009.

A multilocus approach to harvestman (Arachnida: Opiliones) phylogeny with emphasis on biogeography and the systematics of Laniatores.

The internal phylogeny of the arachnid order Opiliones is investigated by including molecular data from five molecular markers for ca. 140 species totalling 43 families of Opiliones. The phylogenetic analyses consisted of a direct optimization (DO) approach using POY v. 4 and sophisticated tree search algorithms as well as a static alignment analysed under maximum likelihood. The four Opiliones suborders were well-supported clades, but subordinal relationships did not receive support in the DO analysis, with the exception of the monophyly of Palpatores (=Eupnoi + Dyspnoi). Maximum-likelihood analysis strongly supported the traditional relationship of Phalangida and Palpatores: (Cyphophthalmi ((Eupnoi + Dyspnoi) Laniatores)). Relationships within each suborder are well resolved and largely congruent between direct optimization and maximum-likelihood approaches. Age estimates for the main Opiliones lineages suggest a Carboniferous diversification of Cyphophthalmi, while its sister group, Phalangida, diversified in the Early Devonian. Diversification of all suborders predates the Triassic, and most major lineages predate the Cretaceous. The following taxonomic changes are proposed. Dyspnoi: Hesperonemastoma is transferred to Sabaconidae. Insidiatores: Sclerobunidae stat. nov. is erected as a family for Zuma acuta. © The Willi Hennig Society 2009.

Organizing phenotypic data-a semantic data model for anatomy.

BACKGROUND: Currently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by non-experts, but the data cannot be parsed by computers either, which in turn hampers their integration across many fields in the life sciences, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data can be recorded, stored, and communicated through the Web in the form of highly formalized and structured directed graphs (semantic graphs) that use ontology terms and URIs as terminology. RESULTS: After introducing an instance-based approach of recording morphological descriptions as semantic graphs (i.e., Semantic Instance Anatomy Knowledge Graphs) and discussing accompanying metadata graphs, I propose a general scheme of how to efficiently organize the resulting graphs in a tuple store framework based on instances of defined named graph ontology classes. The use of such named graph resources allows meaningful fragmentation of the data, which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data. CONCLUSIONS: Morphological data that comply with the here proposed semantic data model will not only be computer-parsable but also re-usable by non-experts and could be better integrated with other sources of data in the life sciences. This would allow morphology as a discipline to further participate in eScience and Big Data.

Levels and building blocks-toward a domain granularity framework for the life sciences.

BACKGROUND: With the emergence of high-throughput technologies, Big Data and eScience, the use of online data repositories and the establishment of new data standards that require data to be computer-parsable become increasingly important. As a consequence, there is an increasing need for an integrated system of hierarchies of levels of different types of material entities that helps with organizing, structuring and integrating data from disparate sources to facilitate data exploration, data comparison and analysis. Theories of granularity provide such integrated systems. RESULTS: On the basis of formal approaches to theories of granularity authored by information scientists and ontology researchers, I discuss the shortcomings of some applications of the concept of levels and argue that the general theory of granularity proposed by Keet circumvents these problems. I introduce the concept of building blocks, which gives rise to a hierarchy of levels that can be formally characterized by Keet's theory. This hierarchy functions as an organizational backbone for integrating various other hierarchies that I briefly discuss, resulting in a domain granularity framework for the life sciences. I also discuss the consequences of this granularity framework for the structure of the top-level category of 'material entity' in Basic Formal Ontology. CONCLUSIONS: The domain granularity framework suggested here is meant to provide the basis on which a more comprehensive information framework for the life sciences can be developed, which would provide the much needed conceptual framework for representing domains that cover multiple granularity levels. This framework can be used for intuitively structuring data in the life sciences, facilitating data exploration, and it can be employed for reasoning over different granularity levels across different hierarchies. It would provide a methodological basis for establishing comparability between data sets and for quantitatively measuring their degree of semantic similarity.

SOCCOMAS: a FAIR web content management system that uses knowledge graphs and that is based on semantic programming.

We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR ('findable', 'accessible', 'interoperable', 'reusable') Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).

Sclerotic bone lesions as a potential imaging biomarker for the diagnosis of tuberous sclerosis complex.

Tuberous-sclerosis-complex (TSC) is associated with a high lifetime risk of severe complications. Clinical manifestations are largely variable and diagnosis is often missed. Sclerotic-bone-lesions (SBL) could represent a potential imaging biomarker for the diagnosis of TSC. In this study, computed tomography (CT) data sets of 49 TSC patients (31 females) were included and compared to an age/sex matched control group. Imaging features of SBLs included frequency, size and location pattern. Sensitivities, specificities and cutoff values for the diagnosis of TSC were established for the skull, thorax, and abdomen/pelvis. In TSC patients, 3439 SBLs were detected, including 665 skull SBLs, 1426 thoracal SBLs and 1348 abdominal/pelvic SBLs. In the matched control-collective, 157 SBLs could be found. The frequency of SBLs enabled a reliable differentiation between TSC patients and the control collective with the following sensitivities and specificities. Skull: ≥5 SBLs, 0.783, 1; thorax: ≥4 SBLs, 0.967, 0.967; abdomen/pelvis: ≥5 SBLs: 0.938, 0.906. SBL size was significantly larger compared to controls (p < 0.05). Based on the frequency, size and location pattern of SBLs TSC can be suspected. SBLs may serve as a potential imaging biomarker in the workup of TSC patients.

Treatment effect of mTOR-inhibition on tissue composition of renal angiomyolipomas in tuberous sclerosis complex (TSC).

PURPOSE: Tuberous sclerosis complex (TSC)-associated renal angiomyolipoma (AML) have a high lifetime risk of acute bleeding. MTOR-inhibitors are a promising novel treatment for TSC-AML, however adequate response to therapy can be difficult to assess. Early changes in MRI signal may serve as a novel early indicator for a satisfactory response to mTOR-inhibitor therapy of AML. MATERIALS AND METHODS: Thirty-eight patients with the definite diagnosis of tuberous sclerosis receiving everolimus therapy and n = 19 patients without specific therapy were included. 1.5 Tesla MRI was performed including sequences with a selective fat suppression. Patients were investigated prior to the initiation of therapy (baseline) and after <3 months (n = 21 patients), 3 to 6 months (n = 32) and 18 to 24 months (n = 28). Signal and size changes of renal AMLs were assessed at all different timepoints. Signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR) and size of angiomyolipomas were evaluated. RESULTS: Signal changes in 273 AMLs were evaluated. A significant and strong decrease of the CNR of AMLs following the initiation of therapy was measured in the fat-suppressed MR sequence at all time points, compared to the baseline: From 7.41±6.98 to 3.84±6.25 (p ≤ 0.05p = 0.002), 3.36±6.93 (p<0.0001), and 2.50±6.68 (p<0.0001) after less than 3 months, 3-6 months or 18-24 months of everolimus treatment, respectively. Also, a significant, however less pronounced, reduction of angiomyolipoma size in the different groups was measured (from baseline 2022.2±2657.7 mm2 to 1854.4±1670.9 mm2 (p = 0.009), 1875.5±3190.1 mm2 (p<0.001), and 1365.8 ± 1628.8 mm2 (p<0.0001) after less than 3 months, 3-6 months or 18-24 months of everolimus treatment, respectively). No significant changes in CNR (p>0.05) and size (p>0.05) were measured in the control group. CONCLUSION: mTOR inhibitor therapy in TSC patients results in an early and pronounced fatty transformation of AMLs on MRI. Fatty transformation could represent a novel early indicator of response to therapy in this patient collective.

Emerging semantics to link phenotype and environment.

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.